If the life expectacy in county A is 65 years with CI of (55, 75) and in county B is 87 years with CI of (40, 98), are the two life expectancies likely to be statistically different?
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If the life expectacy in county A is 65 years with CI of (55, 75) and in county B is 87 years with CI of (40, 98), are the two life expectancies likely to be statistically different?
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- Accounting Today identified the top accounting firms in 10 geographic regions across the United States. All 10 regions reported growth in 2014. The Southeast and Gulf Coast regions reported growth of 12.36% and 5.8%, respectively. A characteristic description of the accounting firms in the Southeast and Gulf Coast regions included the number of partners in the firm. The file AccountingPartners3 contains the number of partners. (Data extracted from bit.ly/1BoMzsv) Assuming that the population variances from both offices are equal, at the 0.10 level of significance, is there evidence of a difference between Southeast region accounting firms and Gulf Coast accounting firms with respect to the mean number of partners? Referring to Table 10-1, the proper conclusion for this test is Question 5 options: 1) at the α = 0.10 level, there is sufficient evidence to indicate a difference in the mean time to clear problems in the two offices.…The distribution of IQ scores of the population is normally distributed with a mean of 100 and a standard deviation of 15 for a particular data value. Since the population is normally distributed all sampling distributions will also be normally distributed. Find the z-scores for each of the following. Make sure to pay attention to the distribution you should use. Write exact answers as decimals. A single person with an IQ of 121: Z-score = Mean of a sample of 4 IQ's is 121: Z-score = 2.80 Mean of a sample of 9 IQ's is 121: Z-score = 1.40 Mean of a sample of 25 IQ's is 121: Z-score = 4.20 Mean of a sample of 100 IQ's is 121: Z-score = Mean of a sample of 625 IQ's is 121: Z-score = 7.00 14.00 35.00A method of estimating earth temperature based on local precipitation is to build models based on 100s of years of earth at several different constant green house gas level (So constant global temperature for 100s of years) , and compare the correlation of statistical relationships between predicted precipitation patterns in a region, and observed precipitation patterns for a region in a few year time period.
- If the life expectacy in county A is 65 years with CI of (55, 75) and in county B is 87 years with CI of (85, 90), are the two life expectancies likely to be statistically different?Ims.tu.edu.sa قائمة القراءة. Remaining Time: 34 minutes, 24 seconds. v Question Completion Status: QUESTION 1 "Suppose X has an exponential distribution with ß=1/2 , then P (X2 2) is " 0.3988 O 0.0183 0.4786 O 0.2916 QUESTION 2 "Suppose X has an exponential distribution with ß=1/4 , then P (Xs1.) is " O 0.9817 O 0.3246 O 0.4105 O 0.5116 QUESTION 3Suppose you are interested in the role of social support in immune function among retired men who live alone. You ask 50 patients to record the number of days they do not see or interact with a friend or family member over a period of 1 month to see whether the number of nonsocial days in a typical month correlates with the number of new illnesses they experience per year. You decide to use the computational formula to calculate the Pearson correlation between the number of nonsocial days in a month and the number of illnesses per year. To do so, you call the number of nonsocial days in a month X and the number of illnesses per year Y. Then, you add up your data values (∑X and ∑Y), add up the squares of your data values (∑X2 and ∑Y2), and add up the products of your data values (∑XY). The following table summarizes your results: ∑X 590 ∑Y 380 ∑XY 4,887 ∑X2 10,456 ∑Y2 4,258 Find the following values: The sum of squares for the number of nonsocial days in a month is SSX=…
- Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables margin, - the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and • home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to 0 when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: regress margin home if team===== "Brooklyn Nets" . Source Model Residual Total margin home _cons SS 1459.95122 15252.0488 16712 df 1459.95122 1 80 190.65061 None of the above 81 206.320988 Coef. Std. Err. 8.439024 3.049595 -5.219512 2.156389 MS t Number of obs F(1, 80) Prob > F R-squared O The Nets lost more games than they won in 2014 P>|t| 2.77 0.007 -2.42 0.018 Adj R-squared = Root…11. In this same article on sleep duration and start time, researchers also considered whether school start time was related to obtaining an adequate amount of sleep. An adequate amountbof sleep was considered at least 8.5 hours of sleep, as recommended by the National Sleep Foundation. The authors used logistic regression models to associate the probability of adequate sleep to school start time. Here are some adapted logistic regression results from this study: In(odds of adequate sleep) = 6, + B,, where x1 = school start time, measured as the number of minutes after 7 AM that the school starts. For this model, B,-0.014, SE(B,)-0.005. - What Is the estimated odds ratlo of adequate sleep, and 95% CI, for students who start at 8:30 AM compared to those who start at 7:30 AM? a. 1.01 (1.00, 1.02) b. 1.52 (1.13, 2.05) C. 2.32 (1.27, 4.22) d. 4.05 (1.49, 11.02)The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x�^=�0+�1�, for predicting a woman's bone density based on her age. 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. Age 44 52 54 62 70 Bone Density 346 342 332 325 323 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6: According to the estimated linear model, if the value of the independent variable is increased by one unit, then the change in the dependent variable yˆ is given by? a. b0 b. b1 c. x d. y Step 4 of 6: Find the estimated value of y when x=52. Round your…
- 26-27. 28-30. 140 y = 4y =Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose a researcher estimates the model and gets the predicted value, cons_hat, and then runs a regression of cons_hat on educ, inc, and hhsize. Which of the following choice is correct and please explain why. A) be certain that R^2 = 1 B) be certain that R^2 = 0 C) be certain that R^2 is less than 1 but greater than 0. D) not be certainProblem : The following system has a radar, a computer and an auxiliary unit connected in series as shown with mean time between failures (MTBF’s) of 91,202 and 500 hours respectively. MTBF=91 HRS MTBF=202 HRS MTBF=500 HRS INPUT RADAR COMPUTER AUXILIARY OUTPUT SYSTEM Determine the following. The unit reliability of each component. System reliability * System MTBF for an operating period of 10 hours