In a regression problem with one output variable and one input variable, we set up two cutpoints z1 and z2 for the input variable and we fit a step function regression model based on these two cutpoints of the input variable. If you write the regression problem in matrix form y = X%*%β + ε, how many rows would the vector β have?

In a regression model, if the variance of the dependent variable, y, conditional on an explanatory variable, x, or Var(y/x), is not constant, a. the t statistics are invalid and the confidence intervals are valid for small sample sizes. b. the t statistics are valid and the confidence intervals are invalid for small sample sizes. c. the t statistics and the confidence intervals are valid no matter how large the sample size. d. the t statistics and the confidence intervals are both invalid no matter how large the sample size. O a. a O b. b О с. C ○ d. d

If we suppose that the weekly price of milk is $3.40 per gallon and MPEP changes the weekly advertising to $300, the best-fitting regression model to estimate the weekly quantity of milk consumed would be Q = 6.52 - 1.614 (3.40) + .005 (300) = 2.533 gallons of milk. What is the elasticity between $5 and $4? Should you lower or raise price to maximize revenue?

Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married females?

The regression equation to predict sales based on temperature is:  Predicted sales = -2419.01+ 98.02 (temperature).    A correct interpretation of the slope would be that 1. as temperature goes up by 1 degree, sales are predicted to go down by 2419.01. 2. as temperature goes down by 1 degree, sales are predicted to go up by 2419.01. 3. as temperature goes up by 1 degree, sales are predicted to go down by 98.02. 4. as temperature goes up by 1 degree, sales are predicted to go up by 98.02. 5. None of the answer choices provides a correct interpretation of the slope.

Instructions: Submit a well-formatted Word, pdf, or similar file in Canvas with your R scripts, regression output, and answers to the questions below. This lab exercise asks you to evaluate the housing market. The data set housingprices40.csv contains all sales of single-family homes in Davis, CA in May 2018 (n = 40). Assume that these homes are a random sample. The dependent variable in the regression model is the natural logarithm of the actual price of each house sold. The regression specification is: In(price;) = B2 + B2 In(estimate;) + B3bdrms; + B4bathrms; +B; In(sqrft;) + B6 In(lotsize}) + Brage; + Bapooli+Bocentrali + & where price is the house selling price, estimate is the estimated housing value from April 2018 from a prominent online site that values homes, bdrms is the number of bedrooms, bathrms is the number of bathrooms, sqrft is the interior square footage, lotsize is size of the lot (in feet), age is the age of the house in years, pool is a binary variable set to…

The data for this question is given in the file 1.Q1.xlsx(see image)  and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college   (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.

The dependent variable in the regression in our cost driver analysis is which of the following? Company sales Total overhead cost for the entire period of time Total overhead cost per month

18 Calculate the least square regression líne equation with the given X and Y values. Consider the values: X Y 60 3.1 61 3.6 62 3.8 63 4 65 4.1 To Find, Least Square Regression Line EquationY = a+ b X

Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the estimated coefficients for each of the variables from part b? Be specific. Note Don't forget to include dummy variables.