\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nOffsets!\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nsmoke_lm <- lm(weight ~ weeks * habit, data = ncbirths)\n\nget_regression_table(smoke_lm)\n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept -5.94 0.484\n2 weeks 0.341 0.013\n3 habit: smoker -1.86 1.63 \n4 weeks:habitsmoker 0.039 0.042\n```\n\n\n:::\n:::\n\n\n::: callout-note\n# Interaction Model\n\nThe \\* means the variables are interacting!\n:::\n\n## Estimated Regression Equations\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept -5.94 0.484\n2 weeks 0.341 0.013\n3 habit: smoker -1.86 1.63 \n4 weeks:habitsmoker 0.039 0.042\n```\n\n\n:::\n:::\n\n\n::: {style=\"font-size: 1.1em; color: #0F4C81;\"}\nWhat is the regression equation for non-smoker mothers?\n:::\n\n. . .\n\n::: {style=\"font-size: 1.1em; color: #0F4C81;\"}\nWhat is the regression equation for smoker mothers?\n:::\n\n## {background-color=\"#B6CADA\"}\n\n
\n\n::: {style=\"font-size: 3em; color: #000000;\"}\nWhat if we have a second numerical explanatory variable?\n:::\n\n
\n\n## {background-color=\"#D4D4D4\"}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=960}\n:::\n:::\n\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nMultiple slopes\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nage_lm <- lm(weight ~ weeks + mage, data = ncbirths)\n\nget_regression_table(age_lm)\n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n term estimate std_error\n \n1 intercept -6.68 0.492\n2 weeks 0.346 0.012\n3 mage 0.02 0.006\n```\n\n\n:::\n:::\n\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #0F4C81;\"}\nHow do you interpret the value of 0.346?\n:::\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #0F4C81;\"}\nHow do you interpret the value of 0.02?\n:::\n\n## {background-color=\"#B6CADA\"}\n\n
\n\n::: {style=\"font-size: 2.5em; color: #000000;\"}\nBut how do we decide if the interaction model is \"best\" without a p-value??????\n:::\n\n
\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nWhen investigating if a relationship differs...\n:::\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #0F4C81;\"}\nAlways start with the \"interaction\" / different slopes model.\n:::\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #b76352;\"}\nIf the slopes look different, you're done!\n:::\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #34605f;\"}\nIf the slopes look similar, then fit the \"additive\" / parallel slopes model.\n:::\n\n## \n\n::: {style=\"font-size: 2em; color: #000000;\"}\nDifferent Enough?\n:::\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=960}\n:::\n:::\n\n\n## {background-color=\"#B6CADA\"}\n\n::: {style=\"font-size: 1.5em; color: #000000;\"}\nWhat if they're not very different?\n:::\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n{fig-align='center' width=960}\n:::\n:::\n\n\n## \n\n::: {style=\"font-size: 2em; color: #000000;\"}\nParallel Slopes\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nlm(average_sat_math ~ perc_disadvan + size, \n data = MA_schools)\n```\n:::\n\n\n\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept 588. 7.61 \n2 perc_disadvan -2.78 0.106\n3 size: medium -11.9 7.54 \n4 size: large -6.36 6.92 \n```\n\n\n:::\n:::\n\n\n## {background-color=\"#B6CADA\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nGroup equations -- Baseline\n:::\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept 588. 7.61 \n2 perc_disadvan -2.78 0.106\n3 size: medium -11.9 7.54 \n4 size: large -6.36 6.92 \n```\n\n\n:::\n:::\n\n\n. . .\n\n$$\\widehat{SAT}_{small} = 588 - 2.78 \\times \\text{percent disadvantaged}$$\n\n## {background-color=\"#B6CADA\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nGroup equations -- Offsets\n:::\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept 588. 7.61 \n2 perc_disadvan -2.78 0.106\n3 size: medium -11.9 7.54 \n4 size: large -6.36 6.92 \n```\n\n\n:::\n:::\n\n\n. . .\n\n::: small\n$$\\widehat{SAT}_{medium} = (588 - 11.9) - 2.78 \\times \\text{percent disadvan}$$\n:::\n\n::: small\n$$\\widehat{SAT}_{medium} = 576.1 - 2.78 \\times \\text{percent disadvan}$$\n:::\n\n. . .\n\n::: small\n$$\\widehat{SAT}_{large} = (588 - 6.36) - 2.78 \\times \\text{percent disadvan}$$\n:::\n\n::: small\n$$\\widehat{SAT}_{large} = 581.64 - 2.78 \\times \\text{percent disadvan}$$\n:::\n\n# Sample Selection Activity\n\n## Find Your Data Group!\n\nOnce you have found other students working on the same dataset, complete [the sample selection activity](https://atheobold.github.io/stat-313-website/activity/week-5-sample-selection.html).\n\n# Midterm Project Work Time\n\n## Steps Before Thursday (STAT 313)\n\n1. Insert the description of your dataset and variables (from the Midterm Proposal) into the \"Introduction\" of your project\n\n2. Pose a research question about your selected variables, **which can be addressed with multiple linear regression**\n\n3. Insert the code to create the required two (or three) visualizations\n\n4. Write a description of what you see in the visualizations\n\n5. Make a decision which model you believe is \"best\"\n\n## Steps Before Thursday (STAT 513)\n\n::: small\n1. Create a [nicely formatted version of your dataset](../project/data-formatting-513.qmd)\n\n2. Insert the description of your dataset and variables (from the Midterm Proposal) into the \"Introduction\" of your project\n\n3. Pose a research question about your selected variables, **which can be addressed with multiple linear regression**\n\n4. Work with Dr. Theobold to write code to read in your data!\n\n5. Insert the code to create the required two (or three) visualizations\n\n6. Write a description of what you see in the visualizations\n\n7. Make a decision which model you believe is \"best\"\n:::\n",
+ "markdown": "---\ntitle: \"Introduction to Multiple Linear Regression\"\nformat: \n revealjs:\n theme: style.scss\neditor: visual\n---\n\n\n\n\n# Midterm Project Week\n\n## Plan for Today\n\n1. Review different types of multiple linear regression models\n\n2. Complete an activity on sample selection\n\n3. Start Midterm Project write-up\n\n## Plan for Wednesday\n\n**No lab** -- focus on getting **all** the coding accomplished for the Midterm Project\n\n## Draft Due by Sunday\n\nTo get everyone feedback on their drafts in a timely manner, the first drafts are due by Sunday.\n\n::: callout-caution\n# Deadline Extension\n\nA deadline extension [**is**]{.underline} permitted for the first draft. Deadline extensions [**are not**]{.underline} permitted for the final version (due next week).\n:::\n\n# Multiple Linear Regression\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 3em; color: #000000;\"}\nBefore...\n:::\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=960}\n:::\n:::\n\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 3em; color: #000000;\"}\nNow...\n:::\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=960}\n:::\n:::\n\n\n## {background-color=\"#B6CADA\"}\n\n \n\n
\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nOffsets!\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nsmoke_lm <- lm(weight ~ weeks * habit, data = ncbirths)\n\nget_regression_table(smoke_lm)\n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept -5.94 0.484\n2 weeks 0.341 0.013\n3 habit: smoker -1.86 1.63 \n4 weeks:habitsmoker 0.039 0.042\n```\n\n\n:::\n:::\n\n\n::: callout-note\n# Interaction Model\n\nThe \\* means the variables are interacting!\n:::\n\n## Estimated Regression Equations\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept -5.94 0.484\n2 weeks 0.341 0.013\n3 habit: smoker -1.86 1.63 \n4 weeks:habitsmoker 0.039 0.042\n```\n\n\n:::\n:::\n\n\n::: {style=\"font-size: 1.1em; color: #0F4C81;\"}\nWhat is the regression equation for non-smoker mothers?\n:::\n\n. . .\n\n::: {style=\"font-size: 1.1em; color: #0F4C81;\"}\nWhat is the regression equation for smoker mothers?\n:::\n\n## {background-color=\"#B6CADA\"}\n\n
\n\n::: {style=\"font-size: 3em; color: #000000;\"}\nWhat if we have a second numerical explanatory variable?\n:::\n\n
\n\n## {background-color=\"#D4D4D4\"}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=960}\n:::\n:::\n\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nMultiple slopes\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nage_lm <- lm(weight ~ weeks + mage, data = ncbirths)\n\nget_regression_table(age_lm)\n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n term estimate std_error\n \n1 intercept -6.68 0.492\n2 weeks 0.346 0.012\n3 mage 0.02 0.006\n```\n\n\n:::\n:::\n\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #0F4C81;\"}\nHow do you interpret the value of 0.346?\n:::\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #0F4C81;\"}\nHow do you interpret the value of 0.02?\n:::\n\n## {background-color=\"#B6CADA\"}\n\n
\n\n::: {style=\"font-size: 2.5em; color: #000000;\"}\nBut how do we decide if the interaction model is \"best\" without a p-value??????\n:::\n\n
\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nWhen investigating if a relationship differs...\n:::\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #0F4C81;\"}\nAlways start with the \"interaction\" / different slopes model.\n:::\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #b76352;\"}\nIf the slopes look different, you're done!\n:::\n\n. . .\n\n::: {style=\"font-size: 1.25em; color: #34605f;\"}\nIf the slopes look similar, then fit the \"additive\" / parallel slopes model.\n:::\n\n## \n\n::: {style=\"font-size: 2em; color: #000000;\"}\nDifferent Enough?\n:::\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=960}\n:::\n:::\n\n\n## {background-color=\"#B6CADA\"}\n\n::: {style=\"font-size: 1.5em; color: #000000;\"}\nWhat if they're not very different?\n:::\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n{fig-align='center' width=960}\n:::\n:::\n\n\n## \n\n::: {style=\"font-size: 2em; color: #000000;\"}\nParallel Slopes\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nlm(average_sat_math ~ perc_disadvan + size, \n data = MA_schools)\n```\n:::\n\n\n\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept 588. 7.61 \n2 perc_disadvan -2.78 0.106\n3 size: medium -11.9 7.54 \n4 size: large -6.36 6.92 \n```\n\n\n:::\n:::\n\n\n## {background-color=\"#B6CADA\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nGroup equations -- Baseline\n:::\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept 588. 7.61 \n2 perc_disadvan -2.78 0.106\n3 size: medium -11.9 7.54 \n4 size: large -6.36 6.92 \n```\n\n\n:::\n:::\n\n\n. . .\n\n$$\\widehat{SAT}_{small} = 588 - 2.78 \\times \\text{percent disadvantaged}$$\n\n## {background-color=\"#B6CADA\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nGroup equations -- Offsets\n:::\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 4 × 3\n term estimate std_error\n \n1 intercept 588. 7.61 \n2 perc_disadvan -2.78 0.106\n3 size: medium -11.9 7.54 \n4 size: large -6.36 6.92 \n```\n\n\n:::\n:::\n\n\n. . .\n\n::: small\n$$\\widehat{SAT}_{medium} = (588 - 11.9) - 2.78 \\times \\text{percent disadvan}$$\n:::\n\n::: small\n$$\\widehat{SAT}_{medium} = 576.1 - 2.78 \\times \\text{percent disadvan}$$\n:::\n\n. . .\n\n::: small\n$$\\widehat{SAT}_{large} = (588 - 6.36) - 2.78 \\times \\text{percent disadvan}$$\n:::\n\n::: small\n$$\\widehat{SAT}_{large} = 581.64 - 2.78 \\times \\text{percent disadvan}$$\n:::\n\n# Sample Selection Activity\n\n## Find Your Data Group!\n\nOnce you have found other students working on the same dataset, complete [the sample selection activity](https://atheobold.github.io/stat-313-website/activity/week-5-sample-selection.html).\n\n# Midterm Project Work Time\n\n## Steps Before Wednesday\n\n1. Insert the description of your dataset and variables (from the Midterm Proposal) into the \"Introduction\" of your project\n\n2. Pose a research question about your selected variables, **which can be addressed with multiple linear regression**\n\n3. Insert the code to create the required two (or three) visualizations\n\n4. Write a description of what you see in the visualizations\n\n5. Make a decision which model you believe is \"best\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
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- "markdown": "---\ntitle: \"Midterm Project Work Day\"\nformat: \n revealjs:\n theme: style.scss\neditor: visual\n---\n\n\n# Reminders About Deadlines\n\n## {background-color=\"#B6CADA\"}\n\n- Lab 3 Revisions are due today (Thursday, February 8)\n\n- Lab 4 Revisions are due next Thursday (February 15)\n\n::: callout-caution\n# Making a copy of your group's Lab 4\n\nIf you were the recorder (typer) for your group, you need to [make your project public](https://atheobold.github.io/stat-313-website/slides/week4-day2.html#copying-the-lab-last-weeks-recorder). If you were not the recorder, you need to [make a copy of your group's project](https://atheobold.github.io/stat-313-website/slides/week4-day2.html#copying-the-lab-everyone-else).\n:::\n\n- The first draft of your Midterm Project is due on [Sunday at midnight]{.underline}.\n\n# Comments from Project Proposals\n\n## For Every Dataset (in STAT 313)\n\n- [Every]{.underine} dataset is included in an R package! You need to find which package that is and load it in!\n\n- If your dataset is associated with a publication, that publication should be referenced in your Introduction!\n\n## Introduction Versus Methods\n\n- The description of your data goes and your research question goes in your Introduction.\n\n- The description of your variables goes at the beginning of your Methods!\n\n## Specific Dataset Advice\n\n- For the `and_vertebrates` data, you should include `species` as an explanatory variable. If you don't you are assuming the same relationship applies to trout AND salamanders.\n\n- For the `hbr_maples` data, you cannot use `year` as a numerical variable. There are only two years of data!\n\n- For the `pie_crabs` data:\n\n - `size` should be the response variable\n - `site` and `latitude` measure the [same]{.underline} thing\n\n# Coding a Multiple Linear Regression\n\n## {background-color=\"#B6CADA\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nStep 0 -- Read in Your Data\n:::\n\n::: columns\n::: {.column width=\"47%\"}\nSTAT 313\n\n::: small\n- Locate what package your data live in (found in the directions for the midterm project proposal)\n\n- Load in the package you need!\n\n- Get started!\n:::\n:::\n\n::: {.column width=\"3%\"}\n:::\n\n::: {.column width=\"50%\"}\nSTAT 513\n\n::: small\n- Upload your formatted dataset to your Midterm Project on Posit Cloud\n- Write the following code to load in your dataset:\n:::\n:::\n:::\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nname_you_want <- read_xlsx(\"name of your dataset.xlsx\")\n```\n:::\n\n\n**OR**\n\n\n::: {.cell}\n\n```{.r .cell-code}\nname_you_want <- read_csv(\"name of your dataset.csv\")\n```\n:::\n\n\n## {background-color=\"#B6CADA\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nStep 1 -- Visualizations\n:::\n\n::: columns\n::: {.column width=\"47%\"}\n[Two Numerical Variables]{.underline}\n\n::: callout-tip\n# Three total visualizations\n:::\n\n::: small\n- Visualize the model with **both** variables -- `color` gradient\n- Visualize **two** simple linear regression models -- one for each variable\n:::\n:::\n\n::: {.column width=\"3%\"}\n:::\n\n::: {.column width=\"50%\"}\n[One Categorical & One Numerical Variable]{.underline}\n\n::: callout-tip\n# Two total visualizations\n:::\n\n::: small\n- Visualize the model using `geom_smooth(method = \"lm\")`\n- Visualize the model using `geom_parallel_slopes()`\n:::\n:::\n:::\n\n::: callout-warning\n# No `theme_classic()`\n\nI like grid lines! Please use `theme_bw()` instead!\n:::\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nStep 2 -- Decide the \"Best\" Model\n:::\n\n::: columns\n::: {.column width=\"45%\"}\n[Two Numerical Variables]{.underline}\n\n::: {style=\"color: #0F4C81; font-size: 0.75em\"}\n- If there appears to be a relationship with the colors -- include **both** variables!\n\n- If the colors are equally dispersed throughout the plot -- choose the [**one**]{.underline} variable that has the [**stronger**]{.underline} relationship (larger slope)!\n:::\n:::\n\n::: {.column width=\"5%\"}\n:::\n\n::: {.column width=\"45%\"}\n[One Categorical & One Numerical Variable]{.underline}\n\n::: {style=\"color: #0F4C81; font-size: 0.75em\"}\n- Look at the plot where the lines are allowed to be different! Does it look like they are?\n:::\n:::\n:::\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nStep 3 -- Fit the regression model with `lm()`\n:::\n\n::: columns\n::: {.column width=\"45%\"}\n[Two Numerical Variables]{.underline}\n\n::: {style=\"color: #0F4C81; font-size: 0.75em\"}\n- Are both variables included? Use a `+` to separate them!\n:::\n:::\n\n::: {.column width=\"5%\"}\n:::\n\n::: {.column width=\"45%\"}\n[One Categorical & One Numerical Variable]{.underline}\n\n::: {style=\"color: #0F4C81; font-size: 0.75em\"}\n- Are the slopes different? You need to fit a different slopes model! Use a `*` to separate the variables!\n\n- Are the slopes similar? You need to fit a parallel slopes model! Use a `+` to separate the variables!\n:::\n:::\n:::\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nStep 4: Get the coefficients with `get_regression_table()`\n:::\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 5em; color: #000000;\"}\nNow interpret!\n:::\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
+ "markdown": "---\ntitle: \"Midterm Project Work Day\"\nformat: \n revealjs:\n theme: style.scss\neditor: visual\n---\n\n\n## Reminders About Deadlines\n\n- Lab 3 Revisions are due today (Wednesday, May 1)\n\n- Lab 4 Revisions are due next Wednesday (May 8)\n\n::: callout-caution\n# Making a copy of your group's Lab 4\n\nIf you were the recorder (typer) for your group, you need to [make your project public](https://atheobold.github.io/stat-313-website/slides/week4-day2.html#copying-the-lab-last-weeks-recorder). If you were not the recorder, you need to [make a copy of your group's project](https://atheobold.github.io/stat-313-website/slides/week4-day2.html#copying-the-lab-everyone-else).\n:::\n\n- The first draft of your Midterm Project is due on [Sunday at midnight]{.underline}.\n\n# Comments from Project Proposals\n\n## For Every Dataset (in STAT 313)\n\n- [Every]{.underine} dataset is included in an R package! You need to find which package that is and load it in!\n\n- If your dataset is associated with a publication, that publication should be referenced in your Introduction!\n\n## Introduction Versus Methods\n\n- The description of your data goes and your research question goes in your Introduction.\n\n- The description of your variables goes at the beginning of your Methods!\n\n## Specific Dataset Advice\n\n- For the `and_vertebrates` data, you should include `species` as an explanatory variable. If you don't you are assuming the same relationship applies to trout AND salamanders.\n\n- For the `hbr_maples` data, you cannot use `year` as a numerical variable. There are only two years of data!\n\n- For the `pie_crabs` data:\n\n - `size` should be the response variable\n - `site` and `latitude` measure the [same]{.underline} thing\n\n# Coding a Multiple Linear Regression\n\n## Step 0 -- Read in Your Data\n\n- Locate what package your data live in (found in the directions for the midterm project proposal)\n\n- Load in the package you need!\n\n- Get started!\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nlibrary(openintro)\n```\n:::\n\n::: {.cell}\n\n:::\n\n\n## Step 1 -- Visualizations\n\n::: columns\n::: {.column width=\"47%\"}\n[Two Numerical Variables]{.underline}\n\n::: callout-tip\n# Three total visualizations\n:::\n\n::: small\n- Visualize the model with **both** variables -- `color` gradient\n- Visualize **two** simple linear regression models -- one for each variable\n:::\n:::\n\n::: {.column width=\"3%\"}\n:::\n\n::: {.column width=\"50%\"}\n[One Categorical & One Numerical Variable]{.underline}\n\n::: callout-tip\n# Two total visualizations\n:::\n\n::: small\n- Visualize the model using `geom_smooth(method = \"lm\")`\n- Visualize the model using `geom_parallel_slopes()`\n:::\n:::\n:::\n\n. . .\n\n::: callout-warning\n# No `theme_classic()`\n\nI like grid lines! Please use `theme_bw()` instead!\n:::\n\n## Step 2 -- Decide the \"Best\" Model\n\n::: columns\n::: {.column width=\"45%\"}\n[Two Numerical Variables]{.underline}\n\n::: {style=\"color: #0F4C81; font-size: 0.75em\"}\n- If there appears to be a relationship with the colors -- include **both** variables!\n\n- If the colors are equally dispersed throughout the plot -- choose the [**one**]{.underline} variable that has the [**stronger**]{.underline} relationship (larger slope)!\n:::\n:::\n\n::: {.column width=\"5%\"}\n:::\n\n::: {.column width=\"45%\"}\n[One Categorical & One Numerical Variable]{.underline}\n\n::: {style=\"color: #0F4C81; font-size: 0.75em\"}\n- Look at the plot where the lines are allowed to be different! Does it look like they are?\n:::\n:::\n:::\n\n## Step 3 -- Fit the regression model you chose with `lm()`\n\n::: columns\n::: {.column width=\"45%\"}\n[Two Numerical Variables]{.underline}\n\n::: {style=\"color: #0F4C81; font-size: 0.75em\"}\n- Are both variables included? Use a `+` to separate them!\n\n:::{.small}\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nmy_model <- lm(size ~ latitude + water_temp, \n data = pie_crab)\n```\n:::\n\n:::\n\n:::\n:::\n\n::: {.column width=\"5%\"}\n:::\n\n::: {.column width=\"45%\"}\n[One Categorical & One Numerical Variable]{.underline}\n\n::: {style=\"color: #0F4C81; font-size: 0.65em\"}\n- Are the slopes different? You need to fit a different slopes model! Use a `*` to separate the variables!\n\n:::{.small}\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nmy_model <- lm(tail_l ~ age + pop, \n data = possum)\n```\n:::\n\n:::\n\n- Are the slopes similar? You need to fit a parallel slopes model! Use a `+` to separate the variables!\n\n:::{.small}\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"false\"}\nmy_model <- lm(weight ~ weeks + habit, \n data = births14)\n```\n:::\n\n:::\n:::\n:::\n:::\n\n## Step 4: Get the coefficients with `get_regression_table()`\n\n## {background-color=\"#D4D4D4\"}\n\n::: {style=\"font-size: 5em; color: #000000;\"}\nNow interpret!\n:::\n",
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- "text": "Lab 3 Revisions are due today (Thursday, February 8)\nLab 4 Revisions are due next Thursday (February 15)\n\n\n\n\n\n\n\nMaking a copy of your group’s Lab 4\n\n\nIf you were the recorder (typer) for your group, you need to make your project public. If you were not the recorder, you need to make a copy of your group’s project.\n\n\n\n\nThe first draft of your Midterm Project is due on Sunday at midnight."
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- "text": "Step 0 – Read in Your Data\n\n\n\nSTAT 313\n\n\nLocate what package your data live in (found in the directions for the midterm project proposal)\nLoad in the package you need!\nGet started!\n\n\n\n\n\nSTAT 513\n\n\nUpload your formatted dataset to your Midterm Project on Posit Cloud\nWrite the following code to load in your dataset:\n\n\n\n\n\n\nname_you_want <- read_xlsx(\"name of your dataset.xlsx\")\n\nOR\n\nname_you_want <- read_csv(\"name of your dataset.csv\")"
+ "text": "Step 4: Get the coefficients with get_regression_table()"
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- "text": "Step 1 – Visualizations\n\n\n\nTwo Numerical Variables\n\n\n\n\n\n\nThree total visualizations\n\n\n\n\n\n\n\n\nVisualize the model with both variables – color gradient\nVisualize two simple linear regression models – one for each variable\n\n\n\n\n\nOne Categorical & One Numerical Variable\n\n\n\n\n\n\nTwo total visualizations\n\n\n\n\n\n\n\n\nVisualize the model using geom_smooth(method = \"lm\")\nVisualize the model using geom_parallel_slopes()\n\n\n\n\n\n\n\n\n\n\nNo theme_classic()\n\n\nI like grid lines! Please use theme_bw() instead!"
+ "text": "Now interpret!"
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- "text": "Step 2 – Decide the “Best” Model\n\n\n\nTwo Numerical Variables\n\n\nIf there appears to be a relationship with the colors – include both variables!\nIf the colors are equally dispersed throughout the plot – choose the one variable that has the stronger relationship (larger slope)!\n\n\n\n\n\nOne Categorical & One Numerical Variable\n\n\nLook at the plot where the lines are allowed to be different! Does it look like they are?"
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- "text": "Step 3 – Fit the regression model with lm()\n\n\n\nTwo Numerical Variables\n\n\nAre both variables included? Use a + to separate them!\n\n\n\n\n\nOne Categorical & One Numerical Variable\n\n\nAre the slopes different? You need to fit a different slopes model! Use a * to separate the variables!\nAre the slopes similar? You need to fit a parallel slopes model! Use a + to separate the variables!"
+ "text": "Step 4: Get the coefficients with get_regression_table()"
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"title": "Introduction to Linear Regression",
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"text": "Relationships Between Variables\n. . ."
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+ "title": "Introduction to Multiple Linear Regression",
+ "section": "Plan for Wednesday",
+ "text": "Plan for Wednesday\nNo lab – focus on getting all the coding accomplished for the Midterm Project"
+ },
+ {
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+ "href": "slides/week5-day1.html#steps-before-wednesday",
+ "title": "Introduction to Multiple Linear Regression",
+ "section": "Steps Before Wednesday",
+ "text": "Steps Before Wednesday\n\nInsert the description of your dataset and variables (from the Midterm Proposal) into the “Introduction” of your project\nPose a research question about your selected variables, which can be addressed with multiple linear regression\nInsert the code to create the required two (or three) visualizations\nWrite a description of what you see in the visualizations\nMake a decision which model you believe is “best”"
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+ "section": "Reminders About Deadlines",
+ "text": "Reminders About Deadlines\n\nLab 3 Revisions are due today (Wednesday, May 1)\nLab 4 Revisions are due next Wednesday (May 8)\n\n\n\n\n\n\n\nMaking a copy of your group’s Lab 4\n\n\nIf you were the recorder (typer) for your group, you need to make your project public. If you were not the recorder, you need to make a copy of your group’s project.\n\n\n\n\nThe first draft of your Midterm Project is due on Sunday at midnight."
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+ "section": "Step 0 – Read in Your Data",
+ "text": "Step 0 – Read in Your Data\n\nLocate what package your data live in (found in the directions for the midterm project proposal)\nLoad in the package you need!\nGet started!\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nlibrary(openintro)"
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+ "section": "Step 1 – Visualizations",
+ "text": "Step 1 – Visualizations\n\n\nTwo Numerical Variables\n\n\n\n\n\n\nThree total visualizations\n\n\n\n\n\n\n\n\nVisualize the model with both variables – color gradient\nVisualize two simple linear regression models – one for each variable\n\n\n\n\n\nOne Categorical & One Numerical Variable\n\n\n\n\n\n\nTwo total visualizations\n\n\n\n\n\n\n\n\nVisualize the model using geom_smooth(method = \"lm\")\nVisualize the model using geom_parallel_slopes()\n\n\n\n\n\n\n\n\n\n\n\nNo theme_classic()\n\n\nI like grid lines! Please use theme_bw() instead!"
+ },
+ {
+ "objectID": "slides/week5-day2.html#step-2-decide-the-best-model",
+ "href": "slides/week5-day2.html#step-2-decide-the-best-model",
+ "title": "Midterm Project Work Day",
+ "section": "Step 2 – Decide the “Best” Model",
+ "text": "Step 2 – Decide the “Best” Model\n\n\nTwo Numerical Variables\n\n\nIf there appears to be a relationship with the colors – include both variables!\nIf the colors are equally dispersed throughout the plot – choose the one variable that has the stronger relationship (larger slope)!\n\n\n\n\n\nOne Categorical & One Numerical Variable\n\n\nLook at the plot where the lines are allowed to be different! Does it look like they are?"
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+ "title": "Midterm Project Work Day",
+ "section": "Step 3 – Fit the regression model with lm()",
+ "text": "Step 3 – Fit the regression model with lm()\n\n\nTwo Numerical Variables\n\n\nAre both variables included? Use a + to separate them!\n\n\n\n\n\nOne Categorical & One Numerical Variable\n\n\nAre the slopes different? You need to fit a different slopes model! Use a * to separate the variables!\nAre the slopes similar? You need to fit a parallel slopes model! Use a + to separate the variables!"
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+ "title": "Midterm Project Work Day",
+ "section": "Step 4: Get the coefficients with get_regression_table()",
+ "text": "Step 4: Get the coefficients with get_regression_table()"
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+ "title": "Midterm Project Work Day",
+ "section": "Step 3 – Fit the regression model you chose with lm()",
+ "text": "Step 3 – Fit the regression model you chose with lm()\n\n\nTwo Numerical Variables\n\n\nAre both variables included? Use a + to separate them!\n\n\n\nmy_model <- lm(size ~ latitude + water_temp, \n data = pie_crab)\n\n\n\n\n\n\nOne Categorical & One Numerical Variable\n\n\nAre the slopes different? You need to fit a different slopes model! Use a * to separate the variables!\n\n\n\nmy_model <- lm(tail_l ~ age + pop, \n data = possum)\n\n\n\nAre the slopes similar? You need to fit a parallel slopes model! Use a + to separate the variables!\n\n\n\nmy_model <- lm(weight ~ weeks + habit, \n data = births14)"
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diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index e530bbdf..150c392c 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -302,7 +302,7 @@
https://atheobold.github.io/stat-313-website/slides/week5-day1.html
- 2024-02-20T19:30:27.725Z
+ 2024-04-26T17:18:27.161Zhttps://atheobold.github.io/stat-313-website/slides/mlr-anova.html
@@ -310,7 +310,7 @@
https://atheobold.github.io/stat-313-website/slides/week5-day2.html
- 2024-02-20T19:30:27.728Z
+ 2024-04-26T17:29:43.815Zhttps://atheobold.github.io/stat-313-website/slides/week3-day1-alt.html
diff --git a/docs/slides/week5-day1.html b/docs/slides/week5-day1.html
index f544588d..74380e9a 100644
--- a/docs/slides/week5-day1.html
+++ b/docs/slides/week5-day1.html
@@ -8,7 +8,7 @@
-
+
STAT 313 / 513 - Introduction to Multiple Linear Regression
@@ -409,8 +409,8 @@
Plan for Today
Start Midterm Project write-up
-
-
Plan for Thursday
+
+
Plan for Wednesday
No lab – focus on getting all the coding accomplished for the Midterm Project
@@ -425,7 +425,7 @@
Draft Due by Sunday
Deadline Extension
-
A deadline extension is permitted for the first draft. Deadline extensions are not permitted for the final version (due next week).
+
A deadline extension is permitted for the first draft. Deadline extensions are not permitted for the final version (due next week).
@@ -690,8 +690,8 @@
Find Your Data Group!
Midterm Project Work Time
-
-
Steps Before Thursday (STAT 313)
+
+
Steps Before Wednesday
Insert the description of your dataset and variables (from the Midterm Proposal) into the “Introduction” of your project
Pose a research question about your selected variables, which can be addressed with multiple linear regression
@@ -699,27 +699,23 @@
Steps Before Thursday (STAT 313)
Write a description of what you see in the visualizations
Step 4: Get the coefficients with get_regression_table()
-
+
+
Step 4: Get the coefficients with get_regression_table()
+
+
Now interpret!
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+
+
-
-
+
diff --git a/slides/week5-day1.qmd b/slides/week5-day1.qmd
index cbda8c77..0b0e82c7 100644
--- a/slides/week5-day1.qmd
+++ b/slides/week5-day1.qmd
@@ -35,7 +35,7 @@ my_theme <- theme_bw() +
3. Start Midterm Project write-up
-## Plan for Thursday
+## Plan for Wednesday
**No lab** -- focus on getting **all** the coding accomplished for the Midterm Project
@@ -46,7 +46,7 @@ To get everyone feedback on their drafts in a timely manner, the first drafts ar
::: callout-caution
# Deadline Extension
-A deadline extension is permitted for the first draft. Deadline extensions [**are not**]{.underline} permitted for the final version (due next week).
+A deadline extension [**is**]{.underline} permitted for the first draft. Deadline extensions [**are not**]{.underline} permitted for the final version (due next week).
:::
# Multiple Linear Regression
@@ -368,7 +368,7 @@ Once you have found other students working on the same dataset, complete [the sa
# Midterm Project Work Time
-## Steps Before Thursday (STAT 313)
+## Steps Before Wednesday
1. Insert the description of your dataset and variables (from the Midterm Proposal) into the "Introduction" of your project
@@ -380,20 +380,20 @@ Once you have found other students working on the same dataset, complete [the sa
5. Make a decision which model you believe is "best"
-## Steps Before Thursday (STAT 513)
+
-::: small
-1. Create a [nicely formatted version of your dataset](../project/data-formatting-513.qmd)
+
+
-2. Insert the description of your dataset and variables (from the Midterm Proposal) into the "Introduction" of your project
+
-3. Pose a research question about your selected variables, **which can be addressed with multiple linear regression**
+
-4. Work with Dr. Theobold to write code to read in your data!
+
-5. Insert the code to create the required two (or three) visualizations
+
-6. Write a description of what you see in the visualizations
+
-7. Make a decision which model you believe is "best"
-:::
+
+
diff --git a/slides/week5-day2.qmd b/slides/week5-day2.qmd
index 71364436..eea9202b 100644
--- a/slides/week5-day2.qmd
+++ b/slides/week5-day2.qmd
@@ -6,13 +6,11 @@ format:
editor: visual
---
-# Reminders About Deadlines
+## Reminders About Deadlines
-## {background-color="#B6CADA"}
+- Lab 3 Revisions are due today (Wednesday, May 1)
-- Lab 3 Revisions are due today (Thursday, February 8)
-
-- Lab 4 Revisions are due next Thursday (February 15)
+- Lab 4 Revisions are due next Wednesday (May 8)
::: callout-caution
# Making a copy of your group's Lab 4
@@ -49,59 +47,59 @@ If you were the recorder (typer) for your group, you need to [make your project
# Coding a Multiple Linear Regression
-## {background-color="#B6CADA"}
-
-::: {style="font-size: 2em; color: #000000;"}
-Step 0 -- Read in Your Data
-:::
-
-::: columns
-::: {.column width="47%"}
-STAT 313
+## Step 0 -- Read in Your Data
-::: small
- Locate what package your data live in (found in the directions for the midterm project proposal)
- Load in the package you need!
- Get started!
-:::
-:::
-::: {.column width="3%"}
-:::
+
+
+
-::: {.column width="50%"}
-STAT 513
+
+
-::: small
-- Upload your formatted dataset to your Midterm Project on Posit Cloud
-- Write the following code to load in your dataset:
-:::
-:::
-:::
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
```{r}
#| eval: false
#| echo: true
-name_you_want <- read_xlsx("name of your dataset.xlsx")
-```
+#| code-line-numbers: false
-**OR**
+library(openintro)
+```
```{r}
#| eval: false
-#| echo: true
+#| echo: false
name_you_want <- read_csv("name of your dataset.csv")
-```
-## {background-color="#B6CADA"}
+name_you_want <- read_xlsx("name of your dataset.xlsx")
+```
-::: {style="font-size: 2em; color: #000000;"}
-Step 1 -- Visualizations
-:::
+## Step 1 -- Visualizations
::: columns
::: {.column width="47%"}
@@ -134,17 +132,15 @@ Step 1 -- Visualizations
:::
:::
+. . .
+
::: callout-warning
# No `theme_classic()`
I like grid lines! Please use `theme_bw()` instead!
:::
-## {background-color="#D4D4D4"}
-
-::: {style="font-size: 2em; color: #000000;"}
-Step 2 -- Decide the "Best" Model
-:::
+## Step 2 -- Decide the "Best" Model
::: columns
::: {.column width="45%"}
@@ -169,11 +165,7 @@ Step 2 -- Decide the "Best" Model
:::
:::
-## {background-color="#D4D4D4"}
-
-::: {style="font-size: 2em; color: #000000;"}
-Step 3 -- Fit the regression model with `lm()`
-:::
+## Step 3 -- Fit the regression model you chose with `lm()`
::: columns
::: {.column width="45%"}
@@ -181,6 +173,19 @@ Step 3 -- Fit the regression model with `lm()`
::: {style="color: #0F4C81; font-size: 0.75em"}
- Are both variables included? Use a `+` to separate them!
+
+:::{.small}
+```{r}
+#| eval: false
+#| echo: true
+#| label: two-num-model
+#| code-line-numbers: false
+
+my_model <- lm(size ~ latitude + water_temp,
+ data = pie_crab)
+```
+:::
+
:::
:::
@@ -190,70 +195,42 @@ Step 3 -- Fit the regression model with `lm()`
::: {.column width="45%"}
[One Categorical & One Numerical Variable]{.underline}
-::: {style="color: #0F4C81; font-size: 0.75em"}
+::: {style="color: #0F4C81; font-size: 0.65em"}
- Are the slopes different? You need to fit a different slopes model! Use a `*` to separate the variables!
+:::{.small}
+```{r}
+#| eval: false
+#| echo: true
+#| label: interaction-model
+#| code-line-numbers: false
+
+my_model <- lm(tail_l ~ age + pop,
+ data = possum)
+```
+:::
+
- Are the slopes similar? You need to fit a parallel slopes model! Use a `+` to separate the variables!
+
+:::{.small}
+```{r}
+#| eval: false
+#| echo: true
+#| label: parallel-model
+#| code-line-numbers: false
+
+my_model <- lm(weight ~ weeks + habit,
+ data = births14)
+```
:::
:::
:::
-
-## {background-color="#D4D4D4"}
-
-::: {style="font-size: 2em; color: #000000;"}
-Step 4: Get the coefficients with `get_regression_table()`
:::
+## Step 4: Get the coefficients with `get_regression_table()`
+
## {background-color="#D4D4D4"}
::: {style="font-size: 5em; color: #000000;"}
Now interpret!
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Comments from Project Proposals
@@ -460,47 +455,39 @@Specific Dataset Advice
Coding a Multiple Linear Regression