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<h1>Regression Model parameter estimation</h1>
<figure>
<img src="assets/img/data-engineering/Linear-reg1.png" alt="" style="max-width: 60%; max-height: 60%;">
<figcaption></figcaption>
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<h3>Content</h3>
<ol>
<li><a href="#introduction">Introduction</a></li>
<li><a href="#Relationship-of-regression-lines">Relationship of regression lines</a></li>
<li><a href="#Types-of-Linear-Regression">Types of Linear Regression</a></li>
<li><a href="#Mathematical-1">Mathematical Explanation</a></li>
<li><a href="#Assumption-of-LR">Assumptions of Linear Regression</a></li>
<li><a href="#evaluation-metrics-for-LR">Evaluation Metrics for Linear Regression</a></li>
<li><a href="#overfit-goodfit-underfit">Overfitting, Good Fit, and Underfitting in Machine Learning</a></li>
<li><a href="#reference">Reference</a></li>
</ol>
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<section id="introduction">
<h2>Intorduction</h2>
<ul>
<li>The values of \(\beta_0\), \(\beta_1\), and \(\sigma^2\) will almost never be known to an investigator.</li>
<li><p>Instead, sample data consists of n observed pairs</p>
<p>(\(x_1\), \(y_1\)), … , (\(x_n \), \(y_n\)),</p>
<p>from which the model parameters and the true regression line itself can be estimated.</p>
</li>
<li><p>The data (pairs) are assumed to have been obtained independently of one another.</p>
<p>where</p>
<p>\(Y_i =\beta_0+\beta_1 x_i + \epsilon_i\) for \(i = 1, 2, … , n\)</p>
<p>and the \(n\) deviations \(\epsilon_1, \epsilon_2, ..., \epsilon_n\)</p>
</li>
<li><p>The “best fit” line is motivated by the principle of least squares, which can be traced back to the German mathematician Gauss (1777–1855):</p>
<img src="assets/img/data-engineering/Multi-lin-reg.png" alt="" style="max-width: 60%; max-height: 60%;">
</li>
</ul>
<p>A line provides the best fit to the data if the sum of the squared vertical distances (deviations) from the observed points to that line is as small as it can be.</p>
<ul>
<li><p>The sum of squared vertical deviations from the points \((x_1, y_1),…, (x_n, y_n)\) to the line is then:</p>
<p>\(f(b_0, b_1) = \sum_{i=1}^n [y_i - (b_0+b_1 x_i)]^2\)</p>
</li>
<li><p>The point estimates of \(\beta_0\) and \(\beta_1\), denoted by \(\hat{\beta}_0\) and \(\hat{\beta}_1\), are called the least squares estimates they are those values that minimize \(f(b_0, b_1)\).</p>
</li>
<li><p>The fitted regression line or least squares line is then the line whose equation is:</p>
<p>\(y = \hat{\beta}_0+\hat{\beta}_1 x\).</p>
</li>
</ul>
</section>
<section id="Estimation-par">
<ul>
<li><p>The minimizing values of b0 and b1 are found by taking partial derivatives of \(f(b_0, b_1)\) with respect to both \(b_0\) and \(b_1\), equating them both to zero [analogously to \(fʹ(b) = 0\) in univariate calculus], and solving the equations</p>
<p>\(\frac{\partial f(b_0, b_1)}{\partial b_0} = \sum 2 (y_i - b_0 - b_1 x_i) (-1) = 0\)</p>
<p>\(\frac{\partial f(b_0, b_1)}{\partial b_1} = \sum 2 (y_i - b_0 - b_1 x_i) (-x_i) = 0\).</p>
</li>
<li><p>Which in term gives two equations:</p>
<p>\(\sum (y_i - b_0 - b_1 x_i) = 0\)</p>
<p>\(\sum (y_i x_i- b_0x_i - b_1 x_i^2) = 0\).</p>
<p>after some simplification, we can get</p>
<p>\(\boxed{b_1 = \hat{\beta}_1 = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sum (x_i - \bar{x})^2} = \frac{S_{xy}}{S_{xx}}}\)</p>
<p>where</p>
<ul>
<li>\(S_{xy}= \sum x_i y_i - \frac{\sum x_i \sum y_i}{n}\) </li>
<li>\(S_{xx} = \sum x_i^2 - \frac{\sum x_i^2}{n}\)</li>
</ul>
<p>(Typically columns for \(x_i, y_i, x_i y_i\) and \(x_i^2\) and constructed and then \(S_{xy}\) and \(S_{xx}\) are calculated.)</p>
</li>
<li><p>The least squares estimate of the intercept \(\beta_0\) of the true regression line is</p>
<p>\(\boxed{b_0 = \hat{\beta}_0 = \frac{\sum y_i - \hat{\beta}_1 \sum x_i}{n} = \bar{y}- \hat{\beta}_1 \bar{x}}\).</p>
</li>
<li><p>The computational formulas for \(S_{xy}\) and \(S_{xx}\) require only the summary statistics \(\sum x_i, \sum y_i, \sum x_i y_i\) and \(\sum x_i^2\) (\(\sum y_i^2\) will be needed shortly for the variance.)</p>
</li>
</ul>
</section>
<section id="Fitted-values">
<h2 id="Fitted-values">Fitted values<a class="anchor-link" href="#Fitted-values">¶</a></h2><h3 id="1.-Fitted-values">1. Fitted values<a class="anchor-link" href="#1.-Fitted-values">¶</a></h3><p>The fitted (or predicted) values \(\hat{y}_1\), \(\hat{y}_2\), ...., \(\hat{y}_n\) are obtained by substituting \(x_1, x_2, ...., x_n\) into the equation of the estimated regression line:</p>
<ul>
<li>\(\hat{y}_1 = \hat{\beta}_0 + \hat{\beta}_1 x_1\)</li>
<li>\(\hat{y}_2 = \hat{\beta}_0 + \hat{\beta}_1 x_1\)</li>
<li>.</li>
<li>.</li>
<li>.</li>
<li>\(\hat{y}_n = \hat{\beta}_0 + \hat{\beta}_1 x_n\)</li>
</ul>
<h3 id="2.-Residuals">2. Residuals<a class="anchor-link" href="#2.-Residuals">¶</a></h3><ul>
<li>The differences \(y_1 - \hat{y}_1\), \(y_2 - \hat{y}_2\), ....., \(y_n - \hat{y}_n\) between the observed and fittted \(y\) values.</li>
<li><p>When the estimated regression line is obtained via the principle of least squares, the sum of the residuals should in theory be zero, if the error distribution is symmetric, since</p>
<p>\(\sum (y_i - (\hat{\beta}_0+ \hat{\beta}_1 x_i)) = n \bar{y}- n \hat{\beta}_0 - \hat{\beta}_1 n \bar{x} = n \hat{\beta}_0 - n \hat{\beta}_0 = 0\).</p>
<ul>
<li>\(y_i-\hat{y}_i > 0 \Rightarrow \) if the point \((x_i y_i)\) lies above the line</li>
<li>\(y_i-\hat{y}_i <> 0 \Rightarrow \) if the point \((x_i y_i)\) lies below the line</li>
</ul>
</li>
<li><p>The residual can be thought of as a measure of deviation and we can summarize the notation in the following way:</p>
<p>\(Y_i - \hat{Y}_i = \hat{\epsilon}_i\)</p>
</li>
</ul>
<h3 id="3.-Estimating-\(%5Csigma%5E2\)-and-\(%5Csigma\)">3. Estimating \(\sigma^2\) and \(\sigma\)<a class="anchor-link" href="#3.-Estimating-\(%5Csigma%5E2\)-and-\(%5Csigma\)">¶</a></h3><ul>
<li><p>The parameter \(\sigma^2\) determines the amount of spread about the true regression line.</p>
<p><img src="assets/img/data-engineering/linear-spread.png" alt="" style="max-width: 60%; max-height: 60%;"></p>
</li>
<li><p>An estimates of \(\sigma^2\) will be used in confidence interval (CI)formulas and hypothesis-testing procedures presented in the next two sections.</p>
</li>
<li>Many large deviations (residuals) suggest a large value of \(\sigma^2\), whereas deviations all of which are small in magnitude suggest that \(\sigma^2\) is small. </li>
<li><p><strong>Error sum of squares (SSE):</strong> The error sum of squares SSE can be interpreted as a measure of how much variation in y is left unexplained by the model—that is, how much cannot be attributed to a linear relationship.</p>
<p>The SSE (equivalently, residual sum of squares), denoted by SSE is:</p>
<p>\(\boxed{{\rm SSE} = \sum (y_i - \hat{y}_i)^2 = \sum [y_i - (\hat{\beta}_0+\hat{\beta}_1 x_i)]^2}\)</p>
<p>and the estimates of \(\sigma^2\) is</p>
<p>\(\hat{\sigma}^2 =s^2 = \frac{\text{SSE}}{n-2} = \frac{\sum (y - \hat{y}_i)^2}{n-2} = \frac{1}{n-2} \sum_{i=1}^n \hat{e}_i^2\).</p>
<p>(Note that the homoscedasticity assumption comes into play here).</p>
</li>
<li><p>The divisor \(n – 2\) in \(s^2\) is the number of degrees of freedom \((df)\) associated with SSE and the estimate \(s^2\).</p>
</li>
<li>This is because to obtain \(s^2\), the two parameters \(\beta_0\) and \(\beta_1\) must first be estimated, which results in a loss of \(2\) \(df\) (just as \(\mu\) had to be estimated in one sample problems, resulting in an estimated variance based on \(n – 1\) df in our previous t-tests).</li>
<li><p>Computation of SSE from the defining formula involves much tedious arithmetic, because both the predicted values and residuals must first be calculated.</p>
<p><img 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" alt="image.png"></p>
<ul>
<li><p>The points in the first plot all fall exactly on a straight line. In this case, all (\(100\%\)) of the sample variation in <em>y</em> can be attributed to the fact that <em>x</em> and <em>y</em> are linearly related in combination with variation in <em>x</em>.</p>
</li>
<li><p>The points in the second plot do not fall exactly on a line, but compared to overall y variability, the deviations from the least squares line are small.</p>
</li>
<li><p>It is reasonable to conclude in this case that much of the observed y variation can be attributed to the approximate linear relationship between the variables postulated by the simple linear regression model.</p>
</li>
<li>When the scatter plot looks like that in the third plot, there is substantial variation about the least squares line relative to overall y variation, so the simple linear regression model fails to explain variation in y by relating y to x.</li>
</ul>
<p>In the first plot SSE = 0, and there is no unexplained variation, whereas unexplained variation is small for second, and large for the third plot.</p>
</li>
</ul>
<h3>4. Total sum of squares (SST) or Total Variation<a class="anchor-link" href="#4.-Total-sum-of-squares-(SST)-or-Total-Variation">¶</a></h3><ul>
<li><p>A quantitative measure of the total amount of variation in observed y values is given by the total sum of squares.</p>
<p>\(\boxed{{\rm SST} = S_{yy} = \sum (y_i -\bar{y})^2 = \sum y_i^2 - \frac{(\sum y_i)^2}{n}}\).</p>
</li>
<li><p>The SST is the sum of squared deviations about the sample mean of the observed y values – when no predictors are taken into account</p>
</li>
</ul>
<h4>4.1. Difference between SST and SSE:<a class="anchor-link" href="#4.1.-Difference-between-SST-and-SSE:">¶</a></h4><ul>
<li><p>The SST in some sense is as bad as SSE can get if there is no regression model (i.e., slope is 0) then</p>
<p>\(\hat{\beta}_0 = \bar{y}- \hat{\beta}_1 \bar{x} \Rightarrow \hat{y} = \hat{\beta}_0+\underbrace{\hat{\beta}_1}_{=0} \bar{x} = \hat{\beta}_0 = \bar{y}\)</p>
<p><img src="assets/img/data-engineering/linear-spread2.png" alt="" style="max-width: 70%; max-height: 70%;"></p>
<p>The SSE < SST unless the horizontal line itself is the least square line.</p>
</li>
</ul>
<h3>5. Coefficient of determination (\(r^2\))<a class="anchor-link" href="#5.-Coefficient-of-determination-(\(r%5E2\))">¶</a></h3><p>\(\boxed{r^2 = 1- \frac{{\rm SSE}}{{\rm SST}}} \Rightarrow \) (a number between 0 and 1.)</p>
<ul>
<li>The ratio SSE/SST is the proportion of total variation that cannot be explained by the simple linear regression mode and \(r^2\) is the proportion of the observed \(y\) variation explained by the model.</li>
<li>It is interpreted as the proportion of observed y variation that can be explained by the simple linear regression model (attributed to an approximate linear relationship between y and x).</li>
<li>The higher the value of \(r^2\), the more successful is the simple linear regression model in explaining y variation.</li>
</ul>
<h3 id="6.-Regression--sum-of-squares-(SSR)">6. Regression sum of squares (SSR)<a class="anchor-link" href="#6.-Regression--sum-of-squares-(SSR)">¶</a></h3><p>The coefficient of determination can be written in a slightly different way by introducing a third sum of squares—regression sum of squares, SSR—given by:</p>
<p>\(\boxed{{\rm SSR} = \sum (\hat{y}_i - \bar{y})^2 = {\rm SST}- {\rm SSE}}\).</p>
<p>Regression sum of squares is interpreted as the amount of total variation that is explained by the model.</p>
<p>Then we have</p>
<p>\(\boxed{r^2 = 1- \frac{{\rm SSE}}{{\rm SST}} = \frac{{\rm SST} - {\rm SSE}}{{\rm SST}} = \frac{{\rm SSR}}{{\rm SST}}} = \frac{\text{Explained Variation}}{\text{Total Variation}}\)</p>
<p>the ratio of explained variation to total variation.</p>
<p><img src="assets/img/data-engineering/linear-spread3.png" alt="" style="max-width: 60%; max-height: 60%;"></p>
</section>
<section id="Hypothesis">
<ul>
<li><p>Testing for significance using the slope, \(\beta_1\):</p>
<p><img src="assets/img/data-engineering/hypo1.png" alt="" style="max-width: 40%; max-height: 40%;"></p>
<ul>
<li>If \(\beta_1 = 0\), then \(y=\beta_0\), no matter what value \(x\) is.</li>
<li>Therefore there is no linear relationship between \(x\) and \(y\) when \(\beta_1 = 0\).</li>
</ul>
</li>
<li><p><strong>Hypothesis test of significance, t-test:</strong></p>
<p>The most commonly encountered pair of hypotheses about \(\beta_1\)</p>
<ul>
<li>\(H_0: \beta_1 = 0 \) </li>
<li><p>\(H_a: \beta_1 \neq 0\).</p>
<p>We are going to see if we have enough evidence to support the alternative hypothesis that the slope is not equal to zero. If we will find a evidence, we will conclude that there is a linear relationship between \(x\) and \(y\).</p>
<p>Here Test statistics: \(\boxed{t=\frac{b_1}{S_{b_1}}}\),</p>
<p>where \(S_{b_1}\) is the standard error for the slope. To calculate this, we use following formula:</p>
<p>\(\boxed{S_{b_1} = \frac{s}{\sqrt{\sum(x_i - \bar{x}_i)^2}}}\),</p>
<p>where \(s = \sqrt{\frac{\text{SSE}}{n-2}}\)</p>
</li>
</ul>
</li>
</ul>
<h3 id="Ways-to-perform-hypothesis-testing">Ways to perform hypothesis testing<a class="anchor-link" href="#Ways-to-perform-hypothesis-testing">¶</a></h3><p>There are several ways to check the null and alternative hypotheses when performing hypothesis testing. Here are some common approaches:</p>
<ol>
<li><p><strong>Critical Value Approach:</strong> The critical value approach involves comparing a test statistic (calculated from the data) to a predetermined critical value based on the chosen significance level (alpha). The steps involved in the critical value approach are as follows:</p>
<ul>
<li>Null hypothesis (H0) and alternative hypothesis (Ha) are defined.</li>
<li>Test statistic (e.g., z-score or t-statistic) is calculated based on the sample data.</li>
<li>Critical value(s) (denoted as z_crit or t_crit) are determined based on the chosen significance level (alpha) and the distribution of the test statistic. </li>
<li><p><strong>Comparison:</strong> If the test statistic falls within the critical region (i.e.,</p>
<ul>
<li>test statistic > critical value for a right-tailed test or </li>
<li><p>test statistic < critical value for a left-tailed test),</p>
<p>reject the null hypothesis in favor of the alternative hypothesis. Otherwise, if the test statistic falls outside the critical region, fail to reject the null hypothesis.</p>
</li>
</ul>
</li>
</ul>
<p>The critical value approach is commonly used in tests such as z-tests and t-tests, where critical values are obtained from standard tables or calculated based on the desired significance level and the test's distribution.</p>
</li>
<li><p><strong>P-Value Approach:</strong> The p-value approach, also known as the probability approach, involves calculating the p-value associated with the observed test statistic. The p-value is the probability of obtaining a test statistic as extreme or more extreme than the observed value, assuming that the null hypothesis is true. The steps involved in the p-value approach are as follows:</p>
<ul>
<li>Null hypothesis (H0) and alternative hypothesis (Ha) are defined.</li>
<li>Test statistic (e.g., z-score or t-statistic) is calculated based on the sample data.</li>
<li>P-value is calculated, representing the probability of obtaining a test statistic as extreme or more extreme than the observed value, assuming the null hypothesis is true.</li>
<li><strong>Comparison:</strong> If the p-value is less than or equal to the chosen significance level (alpha), reject the null hypothesis in favor of the alternative hypothesis. Otherwise, if the p-value is greater than the significance level, fail to reject the null hypothesis.</li>
</ul>
<p>The p-value approach provides a measure of the strength of evidence against the null hypothesis. A smaller p-value indicates stronger evidence against the null hypothesis, suggesting that the observed data is unlikely to occur if the null hypothesis is true. The p-value approach allows for more flexibility in choosing significance levels and can be used with a wide range of statistical tests.</p>
</li>
<li><p><strong>Confidence Interval Approach:</strong></p>
<ul>
<li>Test statistic and its standard error are calculated based on the sample data.</li>
<li>Confidence interval is constructed around the test statistic, typically using the formula: test statistic ± (critical value * standard error).</li>
<li><strong>Comparison:</strong> If the null hypothesis value falls outside the confidence interval, reject the null hypothesis. Otherwise, if the null hypothesis value is inside the confidence interval, fail to reject the null hypothesis.</li>
</ul>
</li>
<li><p><strong>Likelihood Ratio Test:</strong></p>
<ul>
<li>Likelihood of the data under the null hypothesis (L(H0)) and alternative hypothesis (L(Ha)) is calculated based on the sample data.</li>
<li><p>Likelihood ratio is computed as the ratio of the likelihoods:</p>
<p>\(\boxed{\text{likelihood ratio} = \frac{L(Ha)}{L(H0)}}\).</p>
</li>
<li><p><strong>Comparison:</strong> If the likelihood ratio is greater than the critical value corresponding to the chosen significance level or if the p-value associated with the likelihood ratio is less than the chosen significance level, reject the null hypothesis. Otherwise, if the likelihood ratio is not greater than the critical value or the p-value is not less than the significance level, fail to reject the null hypothesis.</p>
</li>
</ul>
</li>
<li><p><strong>Bayesian Approach:</strong></p>
<ul>
<li>Prior probabilities (P(H0) and P(Ha)) are specified for the null and alternative hypotheses.</li>
<li><p>Posterior probabilities (P(H0|data) and P(Ha|data)) are calculated using Bayes' theorem:</p>
<p>\(\boxed{P(H0|\text{data}) = \frac{P(H0) * P(\text{data}|H0)}{P(\text{data})}}\) and</p>
<p>\(\boxed{P(Ha|\text{data}) = \frac{P(Ha) * P(\text{data}|Ha)}{P(\text{data})}}\),</p>
<p>where P(data) is the marginal likelihood.</p>
</li>
<li><p><strong>Comparison:</strong> Decision is made based on the posterior probabilities, such as comparing P(H0|data) to a threshold. If P(H0|data) is lower than the threshold, reject the null hypothesis. Otherwise, if P(H0|data) is higher than or equal to the threshold, fail to reject the null hypothesis.</p>
</li>
</ul>
</li>
</ol>
<h3 id="Test-statistic">Test statistic<a class="anchor-link" href="#Test-statistic">¶</a></h3><h4 id="1.-z-score">1. z-score<a class="anchor-link" href="#1.-z-score">¶</a></h4><ul>
<li>The z-score is used in hypothesis testing when the sample size is large or when the population standard deviation is known.</li>
<li><p>The formula for calculating the z-score is:</p>
<p>\(\boxed{z = \frac{\bar{x} - μ}{(\sigma / \sqrt{n})}}\),</p>
<p>where \(\bar{x}\) is the sample mean, \(\mu\) is the population mean, \(\sigma\) is the population standard deviation, and \(n\) is the sample size.</p>
</li>
<li><p>The z-score follows a standard normal distribution (mean = 0, standard deviation = 1), allowing for comparison to critical values or calculation of p-values.</p>
</li>
</ul>
<h4 id="2.-t-statistic">2. t-statistic<a class="anchor-link" href="#2.-t-statistic">¶</a></h4><ul>
<li>The t-statistic is used when the sample size is small and the population standard deviation is unknown.</li>
<li>The formula for calculating the t-statistic depends on the specific test being performed (e.g., one-sample t-test, independent samples t-test, paired samples t-test).</li>
<li>The t-statistic follows a t-distribution with degrees of freedom (df) determined by the sample size and the specific test being conducted.</li>
<li>Comparison to critical values or calculation of p-values is done using the t-distribution</li>
</ul>
<blockquote><p><strong>One-Sample t-Test:</strong></p>
<p>The one-sample t-test is used to determine if the mean of a single sample significantly differs from a specified population mean.</p>
<ul>
<li><p>The formula for calculating the t-statistic in a one-sample t-test is:</p>
<p>\(\boxed{t = \frac{x-\mu}{s/\sqrt{n}}}\),</p>
<p>where</p>
<ul>
<li>\(x\) is the sample mean, </li>
<li>\(\mu\) is the specified population mean, </li>
<li>\(s\) is the sample standard deviation, and </li>
<li>\(n\) is the sample size.</li>
</ul>
</li>
<li><p>The t-statistic follows a t-distribution with \((n - 1)\) degrees of freedom.</p>
</li>
<li>The null hypothesis (\(H0\)): is that the population mean is equal to the specified value (\(\mu\)), and </li>
<li>the alternative hypothesis (\(Ha\)): is that the population mean is not equal to \(\mu\).</li>
</ul>
<p><strong>Independent Samples t-Test:</strong></p>
<p>The independent samples t-test is used to compare the means of two independent groups and determine if they significantly differ from
each other.</p>
<ul>
<li><p>The formula for calculating the t-statistic in an independent samples t-test is:</p>
<p>\(\boxed{t = \frac{(x_1 - x_2)}{√((s_1^2 / n_1) + (s_2^2 / n_2))}}\),</p>
<p>where</p>
<ul>
<li>\(x_1\) and \(x_2\) are the sample means, </li>
<li>\(s_1\) and \(s_2\) are the sample standard deviations, </li>
<li>\(n_1\) and \(n_2\) are the sample sizes</li>
</ul>
<p>of the two groups.</p>
</li>
<li><p>The t-statistic follows a t-distribution with degrees of freedom calculated using a formula that takes into account the sample sizes
and variances of the two groups.</p>
</li>
<li>The null hypothesis (\(H0\)): is that the means of the two groups are equal, and </li>
<li>the alternative hypothesis (\(Ha\)) is that the means are not equal.</li>
</ul>
<p><strong>Paired Samples t-Test:</strong></p>
<p>The paired samples t-test, also known as the dependent samples t-test, is used to compare the means of two related or paired samples.</p>
<ul>
<li><p>The formula for calculating the t-statistic in a paired samples t-test is:</p>
<p>\(\boxed{t = \frac{(\bar{x}_d - \mu_d)}{(s_d / \sqrt{n})}}\),</p>
<p>where</p>
<ul>
<li>\(\bar{x}_d\) is the mean difference of the paired observations, </li>
<li>\(\mu_d\) is the specified population mean difference (usually \(0\) under the null hypothesis), </li>
<li>\(s_d\) is the standard deviation of the differences, and </li>
<li>\(n\) is the number of paired observations.</li>
</ul>
</li>
<li><p>The t-statistic follows a t-distribution with \((n - 1)\) degrees of freedom.</p>
</li>
<li>The null hypothesis (\(H0\)) is that the mean difference is equal to the specified value (\(\mu_d\), often \(0\)), and </li>
<li>the alternative hypothesis (\(Ha\)) is that the mean difference is not equal to \(\mu_d\).</li>
</ul>
</blockquote>
<h4 id="3.-F-Statistic:">3. F-Statistic:<a class="anchor-link" href="#3.-F-Statistic:">¶</a></h4><ul>
<li>The F-statistic is used in <em>analysis of variance (ANOVA)</em> tests to compare the variability between groups to the variability within groups.</li>
<li>The formula for calculating the F-statistic depends on the specific ANOVA test being performed (e.g., one-way ANOVA, two-way ANOVA).</li>
<li>The F-statistic follows an F-distribution with different degrees of freedom for the numerator and denominator, which are determined by the number of groups and sample sizes.</li>
<li>Comparison to critical values or calculation of p-values is done using the F-distribution.</li>
</ul>
<blockquote><p><strong>Analysis of Variance (ANOVA):</strong> is a statistical technique used to test for significant differences between the means of two or more
groups. ANOVA partitions the total variability in the data into different components to assess the impact of different sources of
variation. Here's an explanation of ANOVA with formulas:</p>
<ol>
<li><strong>One-Way ANOVA:</strong> </li>
</ol>
<ul>
<li>One-Way ANOVA is used when comparing the means of two or more groups on a single independent variable (factor). </li>
<li><p>The formula for calculating the F-statistic in a one-way ANOVA is:</p>
<p>\(F= \frac{SSB / (k - 1)}{(SSW / (n - k))}\),</p>
<p>where</p>
<ul>
<li>\(SSB\) is the between-group sum of squares: \(SSB = \sum n_i (\bar{x}_i-\bar{x})^2\), where \(n_i\) is the sample size of the ith
group, \(\bar{x}_i\) is the mean of the ith group, \(\bar{x}\) is the overall mean.</li>
<li>\(SSW\) is the within-group sum of squares: \(SSW = \sum \sum (x_i - \bar{x}_i)^2\), where \(x_i\) is an individual observation in the
ith group, \(\bar{x}_i\) is the mean of the ith group.</li>
<li>Sometimes, we also need SST = SSB + SSW.</li>
<li>\(k\) is the number of groups, and </li>
<li>\(n\) is the total sample size.</li>
<li>Check example: <a href="https://statkat.com/compute-sum-of-squares-ANOVA.php">https://statkat.com/compute-sum-of-squares-ANOVA.php</a></li>
</ul>
</li>
<li><p>SSB represents the variability between the group means, and SSW represents the variability within each group.</p>
</li>
<li>The F-statistic follows an F-distribution with \((k - 1)\) numerator degrees of freedom and \((n - k)\) denominator degrees of freedom.</li>
<li>The null hypothesis (H0): is that the means of all groups are equal, and </li>
<li>The alternative hypothesis (Ha): is that at least one group mean is different.</li>
</ul>
<ol>
<li><strong>Two-Way ANOVA:</strong></li>
</ol>
<ul>
<li>Two-Way ANOVA is used when comparing the means of two or more groups on two independent variables (factors).</li>
<li>The formula for calculating the F-statistic in a two-way ANOVA involves multiple sources of variation, including main effects and
interaction effects. </li>
<li>The specific formulas depend on the design of the study (e.g., balanced/unbalanced, fixed/random effects).</li>
<li>The F-statistic for each effect (main effect or interaction effect) is calculated by dividing the sum of squares for that effect by
the corresponding degrees of freedom and mean square error.</li>
<li>The F-statistics follow an F-distribution with appropriate degrees of freedom.</li>
<li>The null hypothesis (H0) is that there is no significant effect of the factors or their interaction on the response variable, and</li>
<li>The alternative hypothesis (Ha) is that there is a significant effect.</li>
</ul>
</blockquote>
<h4 id="4.-Chi-Square-Statistic:">4. Chi-Square Statistic:<a class="anchor-link" href="#4.-Chi-Square-Statistic:">¶</a></h4><ul>
<li>The chi-square statistic is used for testing relationships between <em>categorical variables</em> or for testing goodness of fit.</li>
<li>The formula for calculating the chi-square statistic depends on the specific test being conducted (e.g., chi-square test of independence, chi-square goodness of fit test).</li>
<li>The chi-square statistic follows a chi-square distribution with degrees of freedom determined by the number of categories or the degrees of freedom associated with the test.</li>
<li>Comparison to critical values or calculation of p-values is done using the chi-square distribution.</li>
</ul>
<blockquote><p>The Chi-Square statistic is a test statistic used in hypothesis testing to assess the relationship between categorical variables or to
test for goodness of fit. It compares the observed frequencies with the expected frequencies under a specified hypothesis.</p>
<ol>
<li><strong>Chi-Square Test of Independence:</strong></li>
</ol>
<ul>
<li>The Chi-Square test of independence is used to determine if there is a significant association between two categorical variables.</li>
<li><p>The formula for calculating the Chi-Square statistic in a test of independence is:</p>
<p>\(\chi^2 = \sum \frac{(O-E)^2}{E}\),</p>
<p>where \(\sum\) represents the summation symbol, \(O\) is the observed frequency in each cell of a contingency table, and \(E\) is the
expected frequency under the assumption of independence.</p>
</li>
<li>The observed frequencies (\(O\)) are the actual counts of observations in each cell of the contingency table, and the expected
frequencie</li>
<li>(\(E\)) are the counts that would be expected if the two variables were independent.</li>
<li>The Chi-Square statistic follows a Chi-Square distribution with degrees of freedom calculated based on the number of rows and columns
in the contingency table.</li>
</ul>
<ol>
<li><strong>Chi-Square Goodness of Fit Test:</strong></li>
</ol>
<ul>
<li>The Chi-Square goodness of fit test is used to determine if observed categorical data follows a specified distribution or expected
frequencies.</li>
<li><p>The formula for calculating the Chi-Square statistic in a goodness of fit test is:</p>
<p>\(\chi^2 = \sum \frac{(O-E)^2}{E}\),</p>
<p>where \(\sum\) represents the summation symbol, \(O\) is the observed frequency for each category, and \(E\) is the expected frequency
under the null hypothesis.</p>
</li>
<li>The observed frequencies (\(O\)) are the actual counts of observations in each category, and the expected frequencies (\(E\)) are the
counts that would be expected if the null hypothesis is true.</li>
<li>The Chi-Square statistic follows a Chi-Square distribution with degrees of freedom determined by the number of categories minus one.</li>
</ul>
</blockquote>
<h2 id="Calculation">Calculation<a class="anchor-link" href="#Calculation">¶</a></h2><p>To do all these statistics, we may need to find following:</p>
<table>
<thead><tr>
<th>xi</th>
<th>yi</th>
<th>yi_hat=beta0+beta1*xi</th>
<th>Error</th>
<th>Squared error (yi - yi_hat)^2</th>
<th>Deviation yi - ybar</th>
<th>Squared Deviation (yi - ybar)^2</th>
</tr>
</thead>
<tbody>
<tr>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
</tr>
<tr>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
</tr>
<tr>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
</tr>
<tr>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>SSE=...</td>
<td>SST=...</td>
</tr>
</tbody>
</table>
<p>where</p>
<ul>
<li>xi =\(x_i\)</li>
<li>yi = \(y_i\)</li>
<li>yi_hat = beta0 + beta1 * xi = \(\hat{y}_i = \hat{\beta}_0 + \hat{\beta}_1 x_i\) </li>
<li>Error (yi - yi_hat)^2 = Error \((y_i - \hat{y}_i)^2\) </li>
<li>Deviation yi - ybar = Deviation \(y_i - \bar{y}\) </li>
<li>Squared Deviation (yi - ybar)^2 = Squared deviation \((y_i - \bar{y})^2\( </li>
</ul>
<p>and</p>
<table>
<thead><tr>
<th>xi</th>
<th>yi</th>
<th>xi-xbar</th>
<th>yi-ybar</th>
<th>(xi-xbar)(yi-ybar)</th>
<th>(xi-xbar)^2</th>
</tr>
</thead>
<tbody>
<tr>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
</tr>
<tr>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
</tr>
<tr>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
<td>.</td>
</tr>
</tbody>
</table>
<p>to calculate xbar and ybar.</p>
</section>
<section id="Example">
<ul>
<li>You can go to <a href="https://github.com/arunp77/Machine-Learning/tree/main/Projects-ML" target="_blank">following project</a> for a reference for linear regression analysis. </li>
</ul>
</section>
<!-------Reference ------->
<section id="reference">
<h2>References</h2>
<ul>
<li>My github Repositories on Remote sensing <a href="https://github.com/arunp77/Machine-Learning/" target="_blank">Machine learning</a></li>
<li><a href="https://mlu-explain.github.io/linear-regression/" target="_blank">A Visual Introduction To Linear regression</a> (Best reference for theory and visualization).</li>
<li>Book on Regression model: <a href="https://avehtari.github.io/ROS-Examples/" target="_blank">Regression and Other Stories</a></li>
<li>Book on Statistics: <a href="https://hastie.su.domains/Papers/ESLII.pdf" target="_blank">The Elements of Statistical Learning</a></li>
<li><a href="https://www.colorado.edu/amath/sites/default/files/attached-files/ch12_0.pdf">https://www.colorado.edu/amath/sites/default/files/attached-files/ch12_0.pdf</a></li>
</ul>
</section>
<hr>
<div style="background-color: #f0f0f0; padding: 15px; border-radius: 5px;">
<h3>Some other interesting things to know:</h3>
<ul style="list-style-type: disc; margin-left: 30px;">
<li>Visit my website on <a href="sql-project.html">For Data, Big Data, Data-modeling, Datawarehouse, SQL, cloud-compute.</a></li>
<li>Visit my website on <a href="Data-engineering.html">Data engineering</a></li>
</ul>
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