As large U.S. companies flattened their management hierarchies during the 1990s, many of the middle managers who were laid off gave up on “corporate life” and started their own companies or bought franchise operations. This talent migration, along with the fact that most new jobs in the United States were being created by small businesses, cast entrepreneurism into the national spotlight. The business listed in the table were among the fastest growing in the franchise industry. instuction: Propose a regression model that will enable you to compare the mean number of new franchises for the four types of franchises (food, cleaning, accounting/consulting, hospitality) Provide a clear definition for the variables (dependent and independents) being used in the model.

Suppose your firm generated $50.0million$⁢50.0⁢million in sales. What would be the 95%95% prediction interval for your firm's net income? Write your answer using interval notation. Round your answer to 1 decimal place.

Suppose a firm has had the following historic sales figures. Year: 2016 2017 2018 2019 2020 Sales $2,570,000 $3,750,000 $4,380,000 $5,010,000 $5,520,000 What would be the forecast for next year’s sales using regression to estimate a trend?

A researcher found that counties with a high rate of passport ownership also have low rates of obesity, and counties with low rates of passport ownership have high rates of obesity. This is an example of what kind of relationship? Positive Negative Spurious Intervening

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?

La Quinta Inns has a competitive edge over its rivals because it: uses regression analysis to determine which variables most influence profitability. has better television advertisements. picks larger locations than its rivals. builds only along interstate highways. consistently receives four-star ratings for its inns.

In multiple regression analysis, a problem that is not found in simple linear regressions can be caused by the correlation among the independent variables. This problem is called… …multicollaboration …multicollinearity …multicultural …correlation collateral damage

3. Setup: The data set "Healthy Breakfast" contains, among other variables, the Consumer Reports ratings of 77 cereals and the number of grams of sugar contained in each serving. (Data source: Free publication available in many grocery stores. Data set available through the Statlib Data and Story.) Considering "Sugars" as the explanatory variable and "Rating" as the response variable generated the following regression line: Rating = 59.3-2.40 - Sugars. and the ANOVA table Source Regression Error Total SS df MS 8654.7 1 8654.7 102.35 6342.1 75 84.6 14996.8 76 F Questions: (a) Write down the simple linear regression model and its model assumption. (b) State the null and alternative hypotheses for the model. Based on the ANOVA table, draw conclusion based on the p-value for the test. (c) Obtain Srz and the standard error se (3₁) to construct the 95% confidence interval for $₁.

Café Michigan's manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data, which show the numbers of these coffees sold over six different price values: Price $2.70 $3.40 $1.90 $4.10 $3.00 $4.00 Using simple linear regression, and given that the price per cup is $1.80, the forecasted demand for mocha latte coffees will be cups (enter your response rounded to one decimal place). Number Sold 765 510 980 245 320 480