Pearson Moment Product Correlationnote: You may use Excel Eris is trying to figure out if there is a correlation between the cost of the online advertising and the monthly sale of onlinestore. She has the survey results for 7 online stores for the last year. She has now to determine if there is a significantcorrelation between the monthly e-commerce sales and the online advertising costs.The following table represents the survey results from the 7 online stores.Online Monthly E-Online AdvertisingStorecommerce SalesDollars (1000 s)(in 1000 s)368172340156652.84954553311.35562.273761.3Show your step-by-step and complete solution. Use 3 decimal places for all computation and rounding-off of values.

Monthly Ecommerce sales Online advertising dollars 368 1.7 340 1.5 665 2.8 954 5 331 1.3…

Answered: Eris is trying to figure out if there…

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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Question 2 A regression was run to determine if there is a relationship between hours of study per week (x) and the final exam scores (y). The results of the regression were: y=ax+b a=6.992 b=38.9 > r²=0.779689 r=0.883 Use this to predict the final exam score of a student who studies 4 hours per week, and please round your answer to a whole number.
QUESTION 4 Based on the regression equation, we can measure the pearson correlation coefficient predict the value of the dependent variable given a value of the independent variable measure the association between two variables predict the value of the independent variable given a value of the dependent variable.
question 9
QUESTION 5 Suppose you wish to see if there is a relationship between the price (units=$1000) and the rating (units-10 points) of laptop computers tested by Consumer Reports. A regression analysis on 14 computers revealed Ex₁ = 18.20, Ex² = 26.76, Eyi = 111.30, Ey2 = 894.19, and Exiyi = 149.60. What is the interpretation of B₁? Oa. None of the answers is correct b. For an increase of $1000 in price, the mean Consumer Report rating increases by 15.8387 points. OC. For an increase of 10 points in the Consumer Report rating, the mean price of a laptop increases by $158.3871. Od. For an increase of $1000 in price, the mean Consumer Report rating increases by 1.5839 points. Oe. For an increase of 10 points in the Consumer Report rating, the mean price of a laptop increases by $524.8530. QUESTION C ck Save and Submit to save and submit. Click Save All Answers to save all answers. F1 F2 20 F3 $ 000 000 F4 % S F5 MacBook Pro A 6 F6 & F7 DII * FB ∞
Number 103 part D and E
(d) State the interquartile range for this data and use it in a calculation to determine whether the highest score can be classified as an outlier.
The parallel trends assumption is a crucial assumption for which the following estimators? Multiple regression Differences-in-Differences First differences Instrumental Variable
Options for question 1)strong or weak Options for question 2)logarithmic,something else,quadratic,exponential or linear
eat out or in The overall change in the consumer price index is a weighted average of changes in various categories. on november 19, 2011 the boston globe reported the data displayed in figure 5.7. the change in the CPI for food is a weighted average of the change in the cost of food at home and the change in the cost of food away from home. find the weights.
If a predictor variable (x) is highly correlated with another predictor variable (x), the variables are providing essentially the same information. This is best referred to as multi-collinearity. Group of answer choices True False
Which of these analysis helps find the "best fitting" equation for future predictions? Group of answer choices Regression ANOVA Correlation t-test
Item9 eBook Item 9 The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 9.601 3.153 3.045 0.010 x1 0.221 0.110 2.009 0.000 x2 − 1.168 0.577 − 2.024 0.028 x3 − 0.106 0.073 − 1.452 0.114 x4 0.631 0.361 1.748 0.001 x5 − 0.043 0.029 − 1.483 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 1,844.31 368.9 9.37 0.000 Residual Error 56 2,205.62 39.39 Total 61 4,049.93 x1 is the number of architects employed by the company. x2 is the number of engineers employed by the company. x3 is the number of years involved with health care projects. x4 is the number of states in which the firm operates. x5 is the percent of the firm’s work that is health…

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