Question

# R code goes on the line after each question prompt. #

# CG Q0 # Read in the data as tlmrk. # CG Q1 # Use the nrow() function to find the nummber of customer records.
# CG Q2 # How many customers in this dataset subscribed to a term deposit product? # CG Q3 # Find the percent of customers that subscribed to a term deposit product.
# CG Q4a # Fit a logistic regression model for subscribe modeled by all other possible ########## variables in the data set plus the variable durmin squared using the code below. fit <- glm(subscribe ~ . + I(durmin²), data=tlmrk, family="binomial")
# CG Q4b # Use length() combined with the coef() function to find how many ########## coefficients were estimated by glm().
# CG Q5 # Find the R-squared for the fitted regression by referring to the ######### deviances from summary.glm (use the code from youe lecture examples).
# CG Q6a # Customer 27 in our dataset did not end up subscribing to the term deposit. ########## Let's use our fitted model to see what it would have predicted for this customer. ########## First, create an object called "nd" that that you will pass to the predict() ########## function in the newdata argument.
# CG Q6b # Use your fitted logistic regression and your "newdata" in the predict() ########## function to make a prediction of the probability the customer would subscribe.
# CG Q7 # Use the following code to create a confusion matrix and calculate the PPV. rule <- 1/5 # classification rule yhat <- as.numeric(fit$fitted>rule) # classify subscription status based on your rule table(yhat, actualSubscriptionStatus=tlmrk$subscribe) # confusion matrix # Now, use the confusion matrix to calculate the sensitivity (recall).
# CG Q8 # Now, for a classification rule of 1/12, find the PPV (precision). ######### You should submit 4 separate lines of code. # CG Q9 # Calculate the specificity for a rule of 1/3 using 4 lines of code again. ######### Name the rule object rule3 and your predicted subscription status yhat3.

Answer 1

The coefficient of determination (R-squared) measures the proportion of the variation in the dependent variable that is explained by the independent variable(s). The slope of the regression equation represents the rate of change in the dependent variable for a unit change in the independent variable. To estimate values using the line of best fit, substitute the x-values into the regression equation. Outliers can be identified visually by plotting the data points on a scatterplot.

Step-by-step explanation:

The coefficient of determination, also known as R-squared, measures the proportion of the variation in the dependent variable that is explained by the independent variable(s) in a regression model. It ranges from 0 to 1, with higher values indicating a better fit to the data. For example, if the R-squared value is 0.8, it means that 80% of the variation in the dependent variable can be explained by the independent variable(s).

The slope of the regression equation represents the rate of change in the dependent variable for a unit change in the independent variable. In the given equation, the slope is 2.48, meaning that for every additional day, the predicted sales growth increases by $2.48 thousand.

To estimate the PCINC (per capita income) for 1900 and 2000 using the line of best fit, substitute the respective x-values into the regression equation. For example, for 1900 (x = 1900), calculate ŷ = 101.32 + 2.48(1900). For outliers, you can plot the data points on a scatterplot and visually identify any points that deviate significantly from the general pattern.

Answer 2

The coefficient of determination (R-squared) measures the fit of the regression model. The slope of the regression equation indicates the increase in the dependent variable for each unit increase in the independent variable. The line of best fit can be used to estimate values for specific input variables, and outliers can be detected by examining the residuals.

Step-by-step explanation:

f. The coefficient of determination, also known as R-squared, measures the proportion of the variance in the dependent variable that can be explained by the independent variable(s) in the regression model. It ranges from 0 to 1, where 0 indicates no linear relationship and 1 indicates a perfect fit. A higher R-squared value indicates a better fit of the regression model to the data.

g. In the regression equation, the slope represents the change in the dependent variable for a one-unit increase in the independent variable. In this case, the slope of the regression equation is 2.48, which means that for every additional day, the predicted sales growth increases by 2.48 thousand dollars.

h. To estimate the PCINC (Per Capita Income) for 1900 and 2000 using the line of best fit, substitute the respective values of x (year) into the regression equation. For example, for 1900, x = 1900 - 1900 = 0, so the estimated PCINC would be 101.32 thousand dollars. For 2000, x = 2000 - 1900 = 100, so the estimated PCINC would be 101.32 + 2.48(100) = 351.32 thousand dollars.

i. To determine if there are any outliers, you can examine the residuals of the regression model. A residual is the difference between the observed value and the predicted value. If there are any unusually large or small residuals, they may indicate outliers in the data. Graphical methods, such as a scatterplot of the residuals or a histogram, can help identify outliers.