A1 Demand2WN345678910111213141516171819202122232425262728293031327.388.519.527.509.338.288.757.877.108.007.898.159.108.868.908.879.269.008.757.957.657.278.008.508.759.218.277.677.939.26BPriceDif-0.050.250.600.000.250.200.150.05-0.150.150.200.100.400.450.350.300.500.500.40-0.05-0.05-0.100.200.100.500.60-0.050.000.050.55C Identify F statistic and p-value to testHo : B₁ = 0, Ha: ₁0by setting a = 0.01. State your conclude about the fitness of the model?Prove that F statistic is the square of the t-statistic for testingHo : 31 = 0, Ha : B₁0by showing the F.05 rejection point is equal to the square of the t(2-2).025rejection point.

Answered: Identify F statistic and p-value to…

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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Q: The dataset below provides data on sales in dollars and the number of radio and TV ads and newspaper ads promoting the concerts for a group of cities. Develop a multiple regression model using the number of radio and TV ads and newspaper ads as the explanatory variables and sales as the response variable. (Solve in Excel) a) Interpret the adjusted R2. Conduct an Overall Test for Significance. State the hypotheses and the P-value and interpret the results of the overall test for significance in context to this dataset. Conduct individual hypothesis tests for slopes. State the hypothesis and the P-values and interpret the results of each test in context to this dataset. Concert Sales Thousands of Thousands of Sales ($1000) Radio&TV ads Newspaper ads $1,119.00 0 40 $973.00 0 40 $875.00 25 25 $625.00 25 25 $910.00 30 30 $971.00 30 30 $931.00 35 35 $1,177.00 35 35 $882.00 40 25 $982.00 40 25 $1,628.00 45 45 $1,577.00 45 45 $1,044.00 50 50…
Q/Fit (y = ax² + (b/x)) to the following data: (x:1, 2, 3, 4), (y: -1.51, 0.99, 3.88, 7.66). *
The correlation between hours of sleep and hours of study is r = 0.14. (a) Calculate r². (Round your answer to four decimal places.) ,2 = = (b) Choose a sentence that interprets value r². 1.96% of the variation in hours of sleep is not explained by hours of study. Variation in hours of study explains about 1.96% of the observed variation in hours of sleep. 1.96% of the variation in hours of sleep is not explained by the variation in hours of study. Hours of study explains about 1.96% of the observed variation in hours of sleep.
Test for the overall significance of the model. Use =0.05. Report the F-test statistic. Round to two decimal places.
Green Auto periodically has a special week-long sale. As part of the advertising campaign, Green runs one or more television commercials during the weekend preceding the sale. Given are seven observations for two variables, x and y. Number of TV Ads (x) 1 3 2 1 3 4 3 Number of Cars Sold (y) 14 24 15 13 27 28 21 (E). Compute the coefficient of determination R^2. Comment on the goodness of fit. (F). Use the F test to test the hypotheses (α = 0.05): ??0: ??1=0; ????: ??1≠0 State the conclusion and the reasons (i.e., rejection rule).
Interpret a P-value: The manufacturer of a medication designed to lower blood pressure claims that the mean systolic blood pressure for people taking their medication is less than 135. To test this claim, blood pressure is measured for a sample of 500 people who are taking the medication. The P-value for testing Ho: μ = 135 versus H₁: µ< 135 is P = 0.001. a. The manufacturer concludes that because the P-value is very small, we can be fairly certain that the mean pressure is less than 135, but we cannot conclude that it is a lot smaller. Is this conclusion justified?
The prices of Rawlston, Inc. stock (y) over a period of 12 days, the number of shares (in 100s) of the company's stocks sold (x1), and the volume of exchange (in millions) on the New York Stock Exchange (x2) are shown below. 4. Perform an F test and determine whether independent variable and dependent variable are related. Fully interpret the meaning. Use α = .05 5. Conduct Residual Analysis and fully interpret the meaning. 6. If on a given day, the number of shares of the company that were sold was 94,500 and the volume of exchange on the New York Stock Exchange was 10 million, what would you expect the price of the stock to be? GRAPH IS SHOWN BELOW
Dont use excel. thanks
Prev 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 3 of 6: Find the estimated value of y when x = 33. Round your answer to three decimal places. Table Copy Data Next
Overproduction of uric acid in the body can be an indication of cell breakdown. This may be an advance indication of illness such as gout, leukemia, or lymphoma.f Over a period of months, an adult male patient has taken seven blood tests for uric acid. The concentration was x = 5.35 mg/dl. The distribution of uric acid in healthy adult males can be assumed to be normal, with o = 1.77 mg/dl. (a) Find a 95% confidence interval for the population mean concentration of uric acid in this patient's blood. What is the margin of error? (Round your answers to two decimal places.) lower limit upper limit margin of error (b) What conditions are necessary for your calculations? (Select all that apply.) O o is unknown O normal distribution of uric acid O n is large Oo is known O uniform distribution of uric acid (c) Interpret your results in the context of this problem. O The probability that this interval contains the true average uric acid level for this patient is 0.05. O The probability that…
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
11. For temperature (x) and number of ice cream cones sold per hour (y). (65, 8), (70, 10), (75, 11), (80,13), (85, 12), (90, 16). Interpret the coefficient of determination. Optional Answers: 1. 88.2% of the variability in the number of cones sold is explained by the least-squares regression model. 2. 93.9% of the variability in the number of cones sold is explained by the least-squares regression model. 3. 88.2% of the variability in the temperature is explained by the least-squares regression model. 4. 93.9% of the variability in the temperature is explained by the least-squares regression model.

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