Question 38True or False: The slope of a linear prediction equation tells you whether the association is positive or negative.TrueFalse

Answered: True or False: The slope of a linear…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Based on the regression equation, we can Multiple Choice measure cause and effect predict the value of the independent variable given a value of the dependent variable measure the association between two variables predict the value of the dependent variable given a value of the independent variable
Example The marketing manager of Cadbury wants to test the effect of price on sales. Regression model: Sales = β0+ β1Price + error 1) In addition to price, the marketing researcher would like to find out whether there is a systematic trend in the sales. In other words, are sales increasing or decreasing over time? In order to test whether there is a systematic time trend in sales, in addition to the effects of price, please: Tip: the dependent variable is still sales. The independent variables would include price and weeknr (to capture time trend). Write the regression model for question 1 use the examle above to guide you?
A least squares regression model performed to predict the selling price of houses found the following equation: Pricê = 169.328+35.3Area + 0.718Lotsize - 6543Age where Price is in dollars, Area is in square feet, Lotsize is in square feet, and Age is in years. The R2 is 0.92. One of the following interpretations is correct. Which is it? Explain why. a.) Each year a house Ages, it is worth $6543 less. b.) Every extra square foot of Area is associated with an additional $35.30 in average price, for houses with a given Lotsize and Age. c.) Every dollar in price means Lotsize increases 0.718 square feet. d.) This model fits 92% of the data points exactly.
How do you figure this out using conjoint analysis in a linear regression? Determine how various attributes impact the purchase of a car. There are four attributes, each with three levels: Brand: Ford = 1, Chrysler = 2, GM = 3 MPG: 15 MPG = 1, 20 MPG = 2, 25 MPG = 3 Horsepower (HP): 100 HP = 1, 150 HP = 2, 200 HP = 3 Price: $18,000 = 1, $21,000 = 2, $24,000 = 3 The nine product profiles ranked in the data file were evaluated by a consumer. (a) For this market segment, rank the product attributes from most important to least important. Below is the data provided. Brand MPG Power Price Rank 1 1 1 1 4 1 2 2 3 7 1 3 3 2 3 2 1 2 2 6 2 2 3 1 2 2 3 1 3 9 3 1 3 3 8 3 2 1 2 5 3 3 2 1 1
Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67 the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?
Write the linear model to test the hypothesis that there is no treatment effect. Clearly describe each term in the model, and the range of the subscripts. Write the null hypothesis that you are testing. Call: lm(formula = score ~ list, data = hearing) Residuals: Min 1Q Median 3Q Max -14.7500 -5.5833 -0.2083 6.3333 16.4167 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 32.750 1.612 20.315 < 2e-16 *** listList2 -3.083 2.280 -1.352 0.17955 listList3 -7.500 2.280 -3.290 0.00142 ** listList4 -7.167 2.280 -3.144 0.00225 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.898 on 92 degrees of freedom Multiple R-squared: 0.1382, Adjusted R-squared: 0.1101 F-statistic: 4.919 on 3 and 92 DF, p-value: 0.00325
Explain what is meant when two variables are positively linesrly related. What would scatter plott look like? Two variables x and y are postively linearly related if when the x value increases , the y value increases or decrease? The points on a scatter plot fall approximately in a-an ascending straight line b- a descending curve c- a descending straight line or d- an ascending curve from left to right?
Give an equation for a linear regression model for these data to predict the weight from the fuel capacity, Identify the vanables in your equation. • Justify how well your linear equation fits the model by discussing a scatter plot of the date and the correlation coetlicient in assessing the fit of your linear model. • Use your model to predict the total weight of a small motorcycle that has a tuel capacity of 6 gallons. and discuss the usefulness of this prediction.
Interaction terms allow us to account for situations where the relationship between one or more independent variables and the dependent variable may change depending on the levels of another variable. True False

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True or False: The slope of a linear prediction equation tells you whether the association is positive or negative. True False