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 Number Sold 765 510 980 245 320 480 D $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).

The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers):   A.What would be the typical linear regression equation for the number of sales?    B.Make the sales forecast for period 15 of next year.    C. Make the sales forecast for period 17 of next year.      D. What is the standard error for the data?

15. Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $115 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions %3D 39 23 117 70 70 49 122 62 64 122 20 11 149 111 18 104 68 129 116 98 95 60 113 208 40 22 288 48 8. Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = (Round to one decimal place as needed.) х. The best predicted gross for a movie with a $115 million budget is $ million. (Round to one decimal place as needed.)

$339,380 $41,768 A national homebuilder builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price, lot cost, and type of home for closings during one month. Complete parts a through c. E Click the icon to view the house sales data a. Develop a multiple regression model for sales price as a function of lot cost and type of home without any interaction term. Create a dummy variable named "Townhouse", where it is equal to 1 when Type = "Townhouse" and 0 otherwise. Determine the coefficients of the regression equation. Sales Price = 108726 + (3.68 ) • Lot Cost + ( - 75063 )• Townhouse (Round the constant and coefficient of Townhouse to the nearest integer as needed. Round all other values to two decimal places as needed.) b. Determine if an interaction exists between lot cost and type of home and find the best model. Use a = 0.1 as the level of significance. First determine whether an interaction exists. Select the…

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?

The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated.          Team  Revenue  Value  Baltimore  245  1000  Boston  370  2100  Chicago White Sox  227  975  Cleveland  207  825  Detroit  254  1125  Houston  175  800  Kansas City  231  700  Los Angeles Angels  304  1300  Minnesota  223  895  New York Yankees  508  3200  Oakland  202  725  Seattle  250  1100  Tampa Bay  188  625  Texas  266  1220  Toronto  226  870  Arizona  211  840  Atlanta  267  1150  Chicago Cubs  302  1800  Cincinnati  227  885  Colorado…