You and a friend play a game where you each toss a balanced coin. If the upper faces on the coins are both tails, you win $9; if the faces are both heads, you win $8; if the coins do not match (one shows a head, the othera tail), you lose $4 (win (-$4)).Calculate the mean and variance of Y, your winnings on a single play of the game. Note that E(Y) > 0.E(Y) = $XXV(Y) =How much should you pay to play this game if your net winnings, the difference between the payoff and cost of playing, are to have mean 0?$X

Let be the winnings price on a single play of the game.If both coins show tails, he wins .If both…

Answered: You and a friend play a game where you…

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 was generated from the sample data of 1010 junior high students regarding the average number of hours they are unsupervised per night, the average number of hours they play video games per night, and their final grades in their math class. The dependent variable is the final grade, the first independent variable (x1x1) is the number of hours unsupervised each night, and the second independent variable (x2x2) is the number of hours of video games each night. Coefficients Standard Error t-Stat p-value Intercept 57.968435 7.227955 8.020032 0.000201 Hours Unsupervised 5.131630 1.314283 3.904510 0.007943 Hours Playing Video Games 1.067855 1.615039 0.661194 0.533040 Write the multiple regression equation for the computer output given. Round your answers to three decimal places.
A scientist wants to develop a model to describe the relationship between the average daily temperature, x °C, and her household’s daily energy consumption, y kWh, in winter. A random sample of the average daily temperature and her household’s daily energy consumption are taken from 10 winter days and shown in the table. x -0.4 -0.2 0.3 0.8 1.1 1.4 1.8 2.1 2.5 2.6 y 28 30 26 25 26 27 26 24 22 21 You may use: Sy=255 Sxy=283.8 Syy= 64.5 Find a value for the product moment correlation coefficient r for this data Explain what the value of the product moment corelation tells you about the relationship between daily temperature and the household energy consumption.
Determine whether the study depicts an observational study or an experiment. A scientist was studying the effects of a new fertilizer on crop yield. She randomly assigned half of the plots on a farm to group one and the remaining plots to group two. On the plots in group one, the new fertilizer was used for a year. On the plots in group two, the old fertilizer was used. At the end of the year the average crop yield for the plots in group one was compared with the average crop yield for the plots in group two.
The owner of a chain of mini-markets wants to compare the sales performance of two of her stores, Store 1 and Store 2. Sales can vary considerably depending on the day of the week and the season of the year, so she decides to eliminate such effects by making sure to record each store's sales on the same 12 days, chosen at random. She records the sales (in dollars) for each store on these days, as shown in the table below. 5 6 7 Day Store 1 Store 2 1 Difference (Store 1 - Store 2) Send data to calculator ✓ 2 3 786 878 827 645 708 500 699 140 Yes No 4 646 802 712 579 527 367 76 115 66 181 133 8 (a) State the null hypothesis H and the alternative hypothesis H₁. H:D H₁:0 (b) Determine the type of test statistic to use. Type of test statistic: (Choose one) ▼ 970 9 10 11 12 618 679 762 635 271 572 324 215 698 530 Based on these data, can the owner conclude, at the 0.05 level of significance, that the mean daily sales of the two stores differ? Answer this question by performing a hypothesis…
For each problem, select the best response. (a) In a scatterplot of the average price of a barrel of oil and the average retail price of a gallon of gasoline, you expect to see A. a positive association. B. very little association. C. a negative association. D. None of the above. (b) If the correlation between two variables is close to 0, you can conclude that a scatterplot would show A. a cloud of points with no visible pattern.B. no straight-line pattern, but there might be a strong pattern of another form. C. a strong straight-line pattern. D. None of the above. (c) You have data for many years on the average price of a barrel of oil and the average retail price of a gallon of unleaded regular gasoline. When you make a scatterplot, the explanatory variable on the x -axis A. is the price of gasoline. B. can be either oil price or gasoline price. C. is the price of oil. D. None of the above.
The following table was generated from the sample data of 1010 junior high students regarding the average number of hours they are unsupervised per night, the average number of hours they play video games per night, and their final grades in their math class. The dependent variable is the final grade, the first independent variable (x1x1) is the number of hours unsupervised each night, and the second independent variable (x2x2) is the number of hours of video games each night. Coefficients Standard Error t-Stat p-value Intercept 57.968435 7.227955 8.020032 0.000201 Hours Unsupervised 5.131630 1.314283 3.904510 0.007943 Hours Playing Video Games 1.067855 1.615039 0.661194 0.533040 Indicate if any of the independent variables could be eliminated at the
Two statistically independent random have variances of Variables X and 0² and new ran- dom T variables Y 54 are + = 25. TWO defined by U = 3x + 4 Y V = 5X-2Y
If a = 1, b 1 = 10, b 2 = 10, X 1 = 2, and X 2 = 3, what would the predicted Y score be?
Suppose you pulled info regarding the sex of the victim and offender for murders in the United States. The data look something like this: Sex of Vic. Sex of Off. F M F 124 421 M 1609 3725 Assume that the sex of the victim is the response variable. What is the relative risk of being killed by female offender for male victims?
Suppose you are interested in seeing whether the total number of days students are absent from high school correlates with their grades. You obtain school records that list the total absences and average grades (on a percentage scale) for 80 graduating seniors. You decide to use the computational formula to calculate the Pearson correlation between the total number of absences and average grades. To do so, you call the total number of absences X and the average grades Y. Then, you add up your data values (ZX and EY), add up the squares of your data values (EX² and ΣY²), and add up the products of your data values (ΣXY). The following table summarizes your results: ΣΧ 380 ΣΥ 5,820 ⠀ ΣΧ2 2,708 ΣΥ2 440,838 ΣΧΥ 26,709 The sum of squares for the total number of absences is SSx = The sum of squares for average grades is SSy = The sum of products for the total number of absences and average grades is SP = The Pearson correlation coefficient is r = Suppose you want to predict average grades…
A random sample of 136 adults were asked to report the number of hours per week the spent on a computer and their number of years of education. The linear model equation below describes the relationship between the mean computers hours and years of education. computers == 9.12 ++ 0.8 ×× education Based on this linear model, which of the following statements is correct? a) An adult who has no years of education is expected to spend 9.12 hours per week on a computer. b) An adult who has 1 more year of education than another is expected to spend 9.12 more hours per week on a computer. c) An adult who spends 1 more hour per week on a computer than another is expected to have had 9.12 more years of education. d) An adult who spend zero hours per week on a computer is expected to have 9.12 years of education.
Solve.

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