In regression modeling, used when the focus is limited to a is single target (dependent) variable and a single independent variable. Question 22 options: simple linear regression multiple linear regression predictive modeling explanatory modeling

Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion. Marketing manager plans to use a multiple regression model to achieve their target. His model considers yearly revenue as the dependent variable. He found that number of people within 2KM (People), Mean household income(income), no of competitors and price as explanatory variables of company yearly revenue. The following is the descriptive statistics and regression output from Excel.       Revenue People Income Competitors Price             Mean 343965.68 5970.26 41522.96 2.8 5.68 Standard Error 5307.89863 139.0845281 582.1376385 0.142857 0.051030203 Median 345166.5 6032 41339.5 3 5.75 Mode #N/A 5917 #N/A 3 6 Standard Deviation 37532.51115 983.47613 4116.334718 1.010153 0.360838027 Sample Variance 1408689393 967225.2984 16944211.51 1.020408 0.130204082 Sum 17198284 298513 2076148…

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…

Linear regression is a highly effective data analysis method that accurately estimates the value of unknown data using related and known data. This is achieved using a linear equation that accurately models the quantitative relationship between the dependent (unknown) and independent (known) variables.  In other words, by looking at how one variable affects another, this method can accurately forecast the value of the dependent variable. It involves choosing the variable to forecast and using another variable to make informed predictions about its value. In experiments, the independent variable is the cause, and its value remains constant while other variables have been modified. On the other hand, the dependent variable is the effect, and changes influence its value in the independent variable. As I began my business, I struggled with determining the appropriate pricing for my products. It was important that the prices were reasonable while allowing for a profit. The pricing had to…

Which of the following statements best describes linear regression? A measure of the degree of dependence between two variables. A statistical technique using pairs of data and identifying a line which ‘best fits’ all of the pairs of observations. A measure of the proportion of changes in the dependent variable which can be explained by a change in the independent variable. Shows individual costs against their associated activity levels but makes no attempt to identify a linear relationship between them.