how to assess the performance of the model.how to make predictions of the outcome of new data,.how to compute simple and multiple regression models in R,.the basics and the formula of linear regression,.Once, we built a statistically significant model, it’s possible to use it for predicting future outcome on the basis of new x values. The goal is to build a mathematical formula that defines y as a function of the x variable. Linear regression (or linear model) is used to predict a quantitative outcome variable (y) on the basis of one or multiple predictor variables (x) (James et al. 2014,P. The R code and the result are shown below: Solution: As the first step read the variables price and supply and use cor.test function on the variable pair. The plot of \(y = f(x)\) is named the linear regression curve. It can be used only when x and y are from normal distribution. It’s also known as a parametric correlation test because it depends to the distribution of the data. Pearson correlation (r), which measures a linear dependence between two variables (x and y). There are different methods to perform correlation analysis: 6.5.2 Chi-Square test for goodness of fit.6.5.1 Chi-square test for testing independance.6.2.4 Compare means of Large Samples (z-test).6.2.2 Compute one proportion z-test in R.6.1.2 Calculate Confidence Interval in R – t distribution.6.1.1 Calculate confidence interval in R – Normal distribution.6.1 Study of Confidence Intervals for Means of Large and Small Samples.6 Inferential Statistics & Testing of Hypothesis.5.1.3 Preliminary checks before finding the Pearson correlation coefficient.5.1.2 Visualizing the relationship using scatter plot.5.1.1 R method to find correlation coefficient.5 Correlation and Regression Analysis in R.4.3.3 Conditional Probability and Independence.4.3.1 Elements of probability theory-Sample Space, Set Algebra, and Elementary Probability.4 Descriptive Statistics & Probability using R.3.5.5 Violin plot- Another visualization method.3.5.3 Box plot to visualize variation using ggplot2.3.5 Data visialization using advanced library ggplot2 in R. 3.4 Additional tools for data analysis using R.3.3.3 Histogram Extensions and the Rootogram.2.3.1 Perform simple arithmetics using R.2.2 Familiarization of environments in R.1.3 Experiment No: 17- Spearman Rank Correlation.
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