Please help me understand the context of problem and how to solve properly. A researcher is attempting to explore the relationship between study duration per day (0 - 8) and the passing of the science course (Pass=1, Fail=0). For this purpose, a sample of 36 students from Alabama University are chosen. Summary of the logistic regression model has been included. Determine the predicted probability of passing the course for students that study 1 hour per day and determine the Wald test statistic for the intercept.

Suppose there is a significant correlation between two variables. Describe a case under which it might be inappropriate to use the linear regression equation for prediction.

A colleague of yours is completing a final report on the causes of the frequency of cyberbullying.  In this report, she is asked to identify the causes that most strongly impacted the frequency of cyberbullying.  She conducts an OLS regression.  What statistic do you advise her to use in her discussion?  Why?

Two measures of a baseball player's effectiveness as a hitter are the number of hits he makes in a season and thenumber of times he "bats in" a run (knows as "Runs Batted In" or RBIs). Can we predict a batter's RBIs from hisMajor League Baseball batters in 2017.hits? Below is numerical and graphical output from a computer regression of RBIs on Hits for 12 randomly selected Major League Baseball batters in 2017.  Assume that the conditions for inference have been satisfied.(a) Do these data provide convincing evidence that there is a linear relationship between RBIs and Flits for MajorLeague Baseball batters in 2017? (b) Construct a 95% confidence interval for the slope of the population regression line for predicting RBIs from Hits.

You are analyzing a dataset with 932 datapoints. You decide to create a linear regression model with this dataset, using 18 predictor variables. Using this information, what is the degrees of freedom associated with the sum of squares total (total sum of squares) of your analysis?

A rural state wants to encourage high school graduates to continue their education and attend college.  The state collected information on a random sample of high school seniors from across the state 7 years ago and is now observing how many years of education they completed.  They believe students decide to achieve more education when they are more capable, have easier access to college education, and the opportunity cost of attending are lower.  To explore the factors that affect the years of education completed they have used multiple regression to estimate the years of completed education as a function of: Unemployment rate - the unemployment rate in the county (3.9 – 16.8) County Hr Wage - average starting hourly manufacturing wage in the county Test - student score on college admission test (0 to 100 scale) Dist to college - Distance to near college (measured in 100’s of miles) Tuition - Tuition charged at nearest state university (measured in $1000s)…

what is the regression equation for all predictors? I

Which statement is not correct for multiple regression model? When we interpret this categorical variable, we would say the change we observe when one switches from the reference category to the specific category. The reference category will not be displayed in our model results. If we include a categorical variable with more than two values (e.g., religion) as an independent variable, we want to include a dummy variable for all categories except for two. We can include more than two categorical variables in the model. The value of the independent variable not included in the model is called "reference category."

Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. A family purchases a 2000 square foot home and plans to make extensions totalling 500 square feet. The house currently has a pool, and a real estate agent has reported that the house is in excellent condition. However, the house does not have a view, and this will not change as a result of the extensions. According to the results in column (1), what is the expected DOLLAR increase in the price of the home due to the planned extensions?

The following data is the number of tickets sold and the jackpot value for the Mega Millions lottery. The jackpot value is in millions of dollars and the number of tickets sold is in millions. Jackpot value 334 127 300 227 202 180 164 145 255 Number of tickets sold 54 16 41 27 23 18 18 16 26 r= 0.947 tickets sold -10.87 +0.17 (jackpot value) Which is the independent variable and which is the dependent variable? X= independent variable = Y= dependent variable Interpret ². Interpret the slope. Predict the number of tickets sold when the jackpot value is 500 million dollars. Would you feel comfortable using the model for this value? Why or why not.