Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn't evidence that the advertising is impacting sales. What side would you take and why?
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- 2. Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn’t evidence that the advertising is impacting sales. What side would you take and why? Note that this question squarely hits the idea that stats is part science/part art...Being able to read regression results can help the manager use the information to make right decisions particularly in developing a marketing strategy. Assume that you are interested in finding whether the advertisement has a significant positive effect on sales. Which of the following is correct? A. lower standard errors of the estimates are better than higher standard errors B. as a rule of thumb, you are correct 95 % of the time in concluding that there is a positive and significant relationship between the advertising expenditures and sales if the coefficient attached to advertising expenditure is positive and the “t” value is at least 2 C. there is a positive significant relationship between advertising expenditure and sales if both the lower bound and the upper bound of the confidence interval are positive. D. the R2 shows the proportion of the variation in the sales as explained by the model which consists of the advertising expenditure plus some other determinants of sales…Question Three For the past 10 years, you have been observing the sales of your company since you embarked on an aggressive advertising campaign. You have been recording the amounts spent on advertising and the corresponding sales as follows: Year Advert (X) Sales (Y) 2001 10 44 2002 9 40 2003 11 42 2004 12 46 2005 11 48 2006 12 52 2007 13 54 2008 13 58 2009 14 56 2010 15 60 You would like to determine whether a relationship exists between your two variables of interest and therefore decide to run a regression. Specify the estimation model you will use (the econometric form). Identify the independent and the dependent variables. Use an econometric package of your choice (EViews, SPSS, STATA etc) to estimate the model. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). Re-write the specified model in…
- If you are interested in investigating themarginal effect of the percentage change of lot size on the changeof house price, then in your linear regression analysis you should apply thelogarithm transformation on: Select one: a. The house price variable. b. Both the house price variable and the lot size variable. c. The lot size variable. d. The bedroom variable. e. Both the bedroom variable and the house price variable.How do you interpret the R-squared obtained from running this regression?You have data on all individuals in Sweden, including their family size (i.e. their number of children) and their earnings. You want to estimate the effect of family size on earnings, but you suspect your regression will be biased as you do not have information on preferences for how many children people want to have. a) You have information on gender of all children. You have heard that peoplewho have two children of the same gender are more likely to have a third child. Would you be able to use gender composition of the first two children as an instrument for family size? Discuss the assumptions needed for such a model (assume treatment effects are constant). Set up the assumptions mathematically and explain in words what they mean. b) How would you test if the assumptions in b) hold? Is it likely that they hold? (Assume gender composition of the first two children is the only instrument you have access to.)
- For the past 10 years, you have been observing the sales of your company since you embarked on an aggressive advertising campaign. You have been recording the amounts spent on advertising and the corresponding sales as follows: Year Advert (X) Sales (Y) 2001 10 44 2002 9 40 2003 11 42 2004 12 46 2005 11 48 2006 12 52 2007 13 54 2008 13 58 2009 14 56 2010 15 60 You would like to determine whether a relationship exists between your two variables of interest and therefore decide to run a regression. a) Specify the estimation model you will use (the econometric form).b) Re-write the specified model in (a) with values from the regression results and interpret the coefficients.The following data relate the sales figures of the bar in Mark Kaltenbach's small bed-and-breakfast inn in portland, to the number of guest registered that week: week guests bar sales 1 16 $330 2 12 $270 3 18 $380 4 14 $315 a) The simple linear regression equation that relates bar sales to number of guests(not to time) is (round your responses to one decimal place): Bar sales = [___]+[___]X guestsImagine 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 es3mated coefficients for each of the variables from part b? Be specific.
- If we run a regression where y (bankruptcy) = f (factors potentially predicting bankruptcy), what is the dependent variable?A company wants to use regression analysis to forecast the demand for the next quarter.In such a regression model, demand would be the independent variable. True or false?a. Trueb. FalseA realtor was investigating the price of real estate based on the size of the house in square feet x1 and if the house was within walking distance of an "A" rated public school. The indicator variable is defined as x = 1 if the house is within walking distance of an "A" rated public school and x = 0 if the house is NOT within walking distance of an "A" rated public school. If there was interaction in the regression problem, an appropriately fit regression model would have…? a) A different slope and different y-intercept for those within walking distance and those not. b) A different y-intercept for those that were within walking distance and those that were not; the slope would not change. c) A different slope, but not a different y-intercept for those within walking distance and those not. d) Cannot be determined