You will use 'Admission_Predict.csv' for Assignment-3. This dataset includes the data of the applicants of an academic program. Each application has a unique serial number, which represents a particular student. The dataset contains several parameters which are considered important during the application for Masters Programs.

The parameters included are :

1) GRE Scores (out of 340)

2) TOEFL Scores (out of 120)

3) University Rating (out of 5)

4) Statement of Purpose (SOP) (out of 5)

5) Letter of Recommendation (LOR) Strength (out of 5)

6) Undergraduate GPA (out of 10)

7) Research Experience (either 0 or 1)

8) Chance of Admit (ranging from 0 to 1)

**Download "Admission_Predict.csv" dataset and load it as 'data'.**

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## Question 1 (30 points in total)

a)

i- Display the first three rows in this dataset.(1 point)

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ii - Display the structure of all variables.(1 point)

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iii - Print the descriptive statistics of the admission data to understand thedata a little better (min, max, mean, median, 1st and 3rd quartiles). (1 point)

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iv - Use a histogram to assess the normality of the 'Chance.of.Admit' variableand explain whether it appears normally distributed or not and why? (1 point)

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b)Create a set of boxplots that shows the distribution of Chance.of.Admit and SOP variables.Use different colors for different SOP scores month. (8 points)

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c)

i- Find the covariance between the "GRE.Score" and the "Chance.of.Admit". (3 points)

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ii- Find the correlation between the "GRE.Score", "TOEFL.Score", "CGPA" and the "Chance.of.Admit". (3 points)

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iii - Interpret the covariance and correlation results obtained from i and ii in terms of the strength and direction of the relationship. (4 points)

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d)Use ggplot() to plot the graphs to see the relationship between each of three variables (GRE.Score, TOEFL.Score, CGPA) with Chance.of.Admit. (8 points)

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## Question 2 (40 points in total)

a) Define the linear regression modelbetweenGRE.Score and Chance.of.Admit (3 points)

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i - Plot the regression (least-square) line on the same plot.(3 points)

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ii - Explain the meaning of the slope and y-intercept for the least-squares regression line in (b). (3 points)

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b) Print the results of this model and interpret the results by following questions:

i -What is the number of observations was the regression run on? (3 points)

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ii -Interpret the R-squared of this regression? (4 points)

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iii -Write the regression equation associated with this regression model? (4 points)

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c) Use the regression line to predict the chance of admit when GRE score 310. (10 points)

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d) Drawing on this linear model between GRE.Score and Chance.of.Admit, what should be GRE score of a student who has 50% of chance of admission? (10 points)

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## Question 3 (30 points in total)

a) Use three variables ('GRE.Score','TOEFL.Score', 'CGPA')to build a multiple linear regression model to predict 'Chance.of.Admit'. Display a summary of your model indicating Residuals, Coefficients, ..., etc. What conclusion can you draw from this summary? (8 points)

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b) Write the regression equation associated with this multiple regression model? (8 points)

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c) Using this model:

i- Find the chance of admit for the 3rd student and 23rd students in the dataset. (4 points)

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ii- Identify which one has higher chance than the other and print the difference between the chance of admit of these two students.(3 points)

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d) Explain the difference between the model in Question 2(b) and thethe model in Question 3(b) (7 points)

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