The data given below shows gains made by atrader in terms of sales growth for Three products A, Band C. Test at 0.05 level of significance whether the three products differ significantly with regardsto increase sales growth.ProductA : 15 22 20 22 18 14 22ProductB : 14 2412 20 32 21 30ProductC: 13 22 20 22 1814 22

Answered: The data given below shows gains made…

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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The following table contains yearly data on the price of beef from 2022 to 2024: Year Price of Beef ($/Kg) 3 5 8 2022 2023 2024 Forecast the price of chicken for 2025 using the following methods: 1. A 3-Year moving average method; 2. A simple exponential smoothing method (use a=0.5); 8. A simple linear trend model.
Two new variables, the market value of the firm (a measure of firm size, in millions of dollars) and stock return (a measure of firm performance, in percentage points), are added to the regression: In(Earnings) = 3.86 – 0.28Female + 0.37In(MarketValue) + 0.004Return, (0.03) (0.04) (0.004) (0.003) n = 46,670, R = 0.345. If MarketValue increases by 1.88%, what is the increase in earnings? If MarketValue increases by 1.88%, earnings increase by 0.70 % (Round your response to two decimal places.) The coefficient on Female is now – 0.28. Why has it changed from the first regression? O A. Female is correlated with the two new included variables. O B. MarketValue is important for explaining In(Earnings). O C. The first regression suffered from omitted variable bias. OD. All of the above. Assume that the coefficient estimated in the second regression is correct. Forget about the effect of the Return variable, whose effect seems small and statistically insignificant. Calculate the correlation…
Interpret the following graphs for multiple linear regression and comment on the validity of model assumptions
A Bivariate Regression was conducted to evaluate the predictive relationship between total years of schooling and annual income. The results of the regression model were F(1,88) = 4.1, p < .05. What can be concluded about these results? Group of answer choices total years of schooling is a significant predictor of annual income. total years of schooling is not a significant predictor of annual income.
In addition to therapy, anti-depressant drugs are used in the treatment of depression. The data below are from a study comparing two anti-depressant drugs. A baseline measurement for symptomatic behavior for depression is taken. After treatment with medication, another measurement taken. The data given in the table is for improvement which is for the reduction in symptomatic behavior.
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Month Number of Accidents Jan 25 Feb 40 Mar 60 Apr 90 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): ŷ-0-0x Using least-squares regression, the forecast for the number of accidents that will occur in the month of May = accidents (enter your response as a whole number).
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Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. A family purchases a 2000 square foot home and plans to make extensions totalling 500 square feet. The house currently has a pool, and a real estate agent has reported that the house is in excellent condition. However, the house does not have a view, and this will not change as a result of the extensions. According to the results in column (1), what is the expected DOLLAR increase in the price of the home due to the planned extensions?
The accompanying scatterplot shows the relationship between the age of an internet user and the amount of time spent browsing the internet per week (in minutes). The accompanying residual plot is also shown along with the QQ plot of the residuals. Choose the statement that best describes whether the condition for Normality of errors does or does not hold for the linear regression model. Choose the statement that best describes whether the condition for Normality of errors does or does not hold for the linear regression model. A.The residual plot displays a fan shape; therefore the Normality condition is not satisfied.B.The QQ plot mostly follows a straight line; therefore the Normality condition is satisfied.C.The scatterplot shows a negative trend; therefore the Normality condition is satisfied.D.The residual plot shows no trend; therefore the Normality condition is not satisfied.
The results obtained above were primarily due to the mean for the third treatment being noticeably different from the other two sample means. For the following data, the scores are the same as above except that the difference between treatments was reduced by moving the third treatment closer to the other two samples. In particular, 3 points have been subtracted from each score in the third sample. Before you begin the calculation, predict how the changes in the data should influence the outcome of the analysis. That is, how will the F-ratio for these data compare with the F- ratio from above? Treatment A F-ratio= = p-value = 6 5 4 LO 5 5 Treatment B Treatment C 5 6 8 4 6 8 7 7 4 10

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