Why are interaction effects only associated with factorial designs? Examine the following data table and indicate the presence of (a) main effect of A, (b) main effect of B, (c) and interaction effect. Verify this with a graph. Factor B B1 B2 Factor A A1 10 5 A2 5 10

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Explain why a 3 way interaction will have more regression lines than a 2 way interaction.

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A large supermarket chain has invested heavily in data, technology, and analytics. Feeding predictive models with data from an infrared sensor system to anticipate when shoppers will reach the checkout counters, they are able to alert workers to open more checkout lines as needed. This has allowed them to lower their avera checkout time from four minutes to less than 30 seconds. Consider the data in the following table containing 32 observations. Suppose each observation gives the arrival time (measured in minutes before p.m.) and the shopping time (measured in minutes). O Arrival Time (minutes before 6:00 p.m.) Shopping Time (Minutes) 58 21 59 83 54 96 39 13 0 133 55 0 92 12 4 28 Shopping Time (minutes) 23 18 25 28 40 45 33 25 12 53 21 13 37 15 12 17 20 40 60 80 100 120 Arrival Time (Minutes Before 6:00 p.m.) Arrival Time (minutes before 6:00 p.m.) 140 38 AO 15 78 32 23 18 24 38 57 113 30 109 (a) Develop scatter diagram for arrival time as the independent variable. 47 104 72 112…

Jim and Larry run a soup stand. The data below represents the sales of soup that they had on 8 randomly selected days, along with the high temperature on that day.   a) Create a scatter diagram for this data set and comment on whether there is a positive or negative relationship between these variables.b) Calculate the correlation coefficient between temperature and sales.c) Does a linear relationship exist between these variables? Explain to me why or why not this is the case.d) Using excel, calculate the least squares regression equation for this data set. e) Using your equation, predict the soup sales on a day where the temperature is 29 degrees.f) Interpret the slope of this equation.g) Can we use this data set and regression equation to predict the sales on a day when the average temperature is 102 degrees? Why or why not?h) What is the R-squared for this equation? How do you interpret that R-squared?

The condition of an independent variable being correlated to one or more other independent variables is referred to as     nonlinearity.     statistical significance.     multicollinearity.     linearity.

Omitting a variable from our model that has a causal effect on our dependent variable always leads to omitted variable bias.   a. False   b. True

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subject: Quantitative Analysis For Business Please answer both. Use the image for question 10.    10. A hair stylist used market research to learn that members of different age groups have different preferences for positive and negative advertisements. Research participants could mark their preference for only one of the two ads. The positive ad presented a model with attractive hair supported by a message affirming the decision to schedule hair styling services, whereas the negative ad displayed a model with unattractive hair supported by a message describing the need for hair styling services. Results are shown in the table below: What is the probability of a site visitor either preferring negative ads or being a member of age category B?   6% 62% 56% 68% 93% 11. A hair stylist averaged 21 clients per day over the past several months. Typically four of these clients have been male.   What is the probability for the total number of female clients the hair stylist can expect…