In the table below ratings data on a = the quality of the speed of execution and y = overall satisfaction with electronic trades provided the estimated regression equation y = 0.3293 + 0.8537x (AAIIwebsite).BrokerageSpeed SatisfactionScottrade, Inc.4.03.7Charles Schwab3.53.3Fidelity Brokerage Services2.42.6TD Ameritrade3.33.4E*Trade Financial3.22.8Vanguard Brokerage Services3.03.4USAA Brokerage Services2.41.9Thinkorswim3.73.4Wells Fargo Investments3.02.9Interactive Brokers3.63.1Zecco.com3.63.6At the 0.05 level of significance, test whether speed of execution and overall satisfaction are related. show the ANOVA table. What is your conclusion?SourceSum of Squares Degrees Mean SquareFp-valueof Variation (to 4 decimals) of Freedom (to 4 decimals) (to 2 decimals) (to 4 decimals)RegressionErrorTotalP-value is- Select your answer-We - Select your answer - v reject Ho : B, = 0, we conclude that speed of execution and overall satisfaction -Select your answer - vrelated.

Answered: Image /qna-images/answer/4e492ac8-7539-46f5-a41f-701cbc2b27f0.jpg

Answered: in the table below ratings data on a =…

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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For a two-variable linear regression, if the sample correlation between the depen- dent and independent variables is –0.7, then the independent variable explains 49% of the variation in the dependent variable. Is this statement true? Explain your answer.
ev The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 28 33 36 42 45 Number of Bids 1 7 8 9 10 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Table Copy Data Next
A major brokerage company has an office in Miami, Florida. The manager of the office is evaluated based on the number of new clients generated each quarter. Data were collected that show the number of new customers added during each quarter between 2015 and 2018. A multiple regression model was developed with the number of new customers as the dependent and the following four independent variables: Period (1, …, 16): A variable that measures the trend; Q1 = 1 for first quarter, Q1 = 0 otherwise; Q2 = 1 for second quarter, Q2 = 0 otherwise; Q3 = 1 for third quarter, Q3 = 0 otherwise. Questions: 1. Explain each of the four slopes (Period, Q1, Q2, Q3). 2. How many new customers would you expect in the second quarter of the following year (2019)?
Can brand, battery life, and internal storage capacity affect a smartphone's price? Use MegaStat and α = .05 to perform a regression analysis for the Smartphones01BS dataset and answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text.
RequiredPage 92 The company uses the number of professional hours as the cost driver for office support costs. Use an algebraic equation to describe how total office support costs can be estimated. Use a spreadsheet program to perform a regression analysis. Use office support costs as the dependent variable (Y) and the professional hours as the independent variable (X). Determine the total fixed cost per office and variable cost per professional hour. Identify the R2 statistic provided by the Excel program and explain what it means. Mr. Dean plans to open a new branch office in a Dallas suburb. He expects that the monthly professional hours will be 3,000. Estimate the total office support cost for Mr. Dean. What portion of the total cost is fixed and what portion is variable? Explain how multiple regression analysis could be used to improve the accuracy of the cost estimates. *You will use excel to setup a worksheet with your data and run regression analysis.*
Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.8 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) 7.3 7.4 9.8 9.5 8.8 8.5 Weight (kg) 152 187 286 247 237 231 The regression equation is y =+ (x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.)
A car lot wants to predict the # of car accessories they need to sell monthly based on the number of cars sold. The car lot owner randomly selects 12 months of data. The simple linear regression equation is ý = 14 - 2x. The coefficient of determination is 0.6364. What is the correlation coefficient to describe the relationship between these two variables? Multiple Choice
A data ninja has created a multiple regression model. There are 3 independent variables in the model. The variables and their P-values can be found in the table below. Independent VariableP-ValueX1: Advertising0.142X2: Social Media0.006X3: Coupons0.071 QUESTION: Study the P-Values. At 90% confidence level, which independent variable(s) can be proven significant in the model? Group of answer choices X3 only X1 only X2 only X2 and X3
4. Housing Prices in New YorkWe have looked at predicting the price (in s) of New York homes based on the size (in thousands of square feet), using the data in HomesForSaleNY. Two other variables in the dataset are the number of bedrooms and the number of bathrooms. Use technology to create a multiple regression model to predict price based on all three variables: size, number of bedrooms, and number of bathrooms. Price Size Beds Baths 145 1.3 3 1.5 875 2.9 7 3.75 300 1.5 3 2.5 370 1.1 2 1 268 1.5 2 2 1399 4.8 6 5 1125 3.1 3 2.5 299 1.4 3 2 110 1.2 3 1 2999 6 7 8 170 1 2 1 269 1.5 3 1.5 150 1 2 1.5 288 1.8 3 2.1 350 1.3 3 2 120 0.9 1 1 309 2.4 4 2.5 1500 1.5 2 1.5 635 2.5 4 2.5 350 0.9 2 1 459 1.8 4 2.5 275 2.9 4 1.5 275 1.8 3 2 2500 3.7 3 3 187 1.4 3 1.5 238 1.7 3 1.5 155 0.7 1 1 175 1.6 3 1.5 569 3.2 4 2 105 1.2 2 2.5 a) Which of the variables which are significant at the 5% level? b) Which variable is the most…
Do movies of different types have different rates of return on their budgets? Here's a regression of USGross (SM) on Budget for comedies and action movies with an indicator variable. Complete parts (a) through (d). Dependent variable is: USGross ($M) Coefficient SE(Coeff) - 6.78278 16.95 1.00523 Variable Constant Budget ($M) Comedy 24.0373 0.1613 11.73 t-ratio P-value -0.400 0.6907 6.23 <0.0001 2.05 0.0451 a) Write out the regression model. USGross = + ( Budget + (Comedy R-squared = 32.8% R-squared (adjusted) = 31.0% s = 47.51 55 degrees of freedom
Run the Linear Regression Analysis for the following: → Submit Excel FileThe average driving distance (yards) and driving accuracy (percent of drives that land in thefairway) for 8 golfers are recorded in the table below.Rank Driving Distance (yards) Driving Accuracy (%)1 316.3 41.72 304.9 48.83 310.8 42.34 312.5 41.25 294.5 54.76 290.7 54.47 296.9 54.28 295.6 53.9a- Write the equation of a straight-line model relating driving accuracy (y) to drivingdistance (x). Select one of the following:i. Y = β1x + εii. Y = β1x2+ β0iii. Y = β1xiv. Y = β0 + β1x + εb- Fit the model and give the least square prediction equation c- Interpret the estimated y-intercept of the line. Choose the correct answer below:i. Since a drive with 0% accuracy is outside the range of the sample data, the y-intercepthas no practical interpretation.ii. Since a drive with distance 0 yards is outside the range of the sample data, the y-intercept has no practical interpretation.iii. For…
4 A certified financial planner would like to examine the relationship between the age of her clients and the total amount of money they have in their investment accounts. The ages of clients and corresponding account balances for a sample of 15 clients appears in the table. Use the regression analysis output from Excel to determine if there is a positive linear relationship between age of client and investment account balance. Test using a significance level of .01. Balance Age ($1000s) 34 38 57 87 44 129 Coefficients Standard Error t stat 183.89 3.522 56 342 Intercept X variable -226.69 50 770 -1.23 2.87 10.104 44 200 54 430 39 55 67 453 29 76 43 230 76 427 m ib 43 188 70 546 275 Linear Regression and Correlation

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