Example 9: The auto correlation function of a wide-sense stationary rand process is given by 4 t2 + 100 Rxx (t) = t“ + 4 Find the mean and variance of this process.
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A: given data Var(Fahrenheit) = 340.57 Var(Celsius) = ? Conversion between Fahrenheit and Celsius is a…
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A: Here for correlation coefficient we know that Degree of freedom is ( n - 2). Df = 37 - 2 = 35
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Q: The variance of temperatures (Fahrenheit) in Baton Rouge, La. is 167.5. What is this variance if…
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Q: Suppose a local university researcher wants to build a linear model that predicts the freshman year…
A: Solution: From the given information, r=0.454012, sx=107.836402 and sy=0.517403.
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A: Disclaimer: "As per guideline we can only solve one question"
Q: The variance of temperatures (Fahrenheit) in El Paso, Texas is 243.32. What is this variance if temp…
A: We have given that The variance of temperatures (Fahrenheit) in El Paso, Texas is 243.32.…
Q: The variance of temperatures (Fahrenheit) in Birmingham, Ala. is 236.22. What is this variance if…
A: we use Celsius = Fahrenheit*(1/1.80) - (32/1.80)
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Q: The variance of temperatures (Fahrenheit) in Fargo, N.D. is 689.78. What is this variance if temp is…
A: Variance in Farenhite = 689.78 Variance in celsius = ? Relationship : Celsius = Farenhite*(1/1.8)…
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Q: The variance of temperatures (Fahrenheit) in Dodge City, Kan. is 413.49. What is this variance if…
A: given data Var(Fahrenheit) = 413.49 Var(Celsius) = ? Conversion between Fahrenheit and Celsius is a…
Q: Suppose a local university researcher wants to build a linear model that predicts the freshman year…
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Q: The variance of temperatures (Fahrenheit) in Jackson, Miss. is 221.16. What is this variance if temp…
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Q: The variance of temperatures (Fahrenheit) in Des Moines, Iowa is 521.86. What is this variance if…
A: given data Var(Fahrenheit) = 521.86 Var(Celsius) = ? Conversion between Fahrenheit and Celsius is a…
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- Calculate the Karl Pearson's coefficient of correlation of the short-term ocillations for the indices of supply and price of certain commodity given here : Year Year Index of Index of Index of Price Index of Supply Supply Price 2003 91 117 2011 104 77 2004 98 97 2012 98 93 2005 95 102 2013 100 89 2006 92 108 2014 108 83 2007 93 105 2015 116 78 2008 96 96 2016 114 84 2009 102 77 2017 111 93 2010 107 68 [Take 5-yearly moving average and ignore decimals in computing the average.]The variance of temperatures (Fahrenheit) in Mobile, Ala. is 165.16. What is this variance if temp is re-expressed in Celsius? [Hint: Conversion between Fahrenheit and Celsius is a linear transformation: Fahrenheit = Celsius*1.80 + 32, or Celsius = Fahrenheit*(1/1.80) - (32/1.80)]4
- The variance of temperatures (Fahrenheit) in Buffalo, N.Y. is 362.18. What is this variance if temp is re-expressed in Celsius? [Hint: Conversion between Fahrenheit and Celsius is a linear transformation: Fahrenheit = Celsius*1.80 + 32, or Celsius = Fahrenheit*(1/1.80) - (32/1.80)]Are MRI count and IQ linearly related? Because the correlation coefficient for females is negative/positiveand the absolute value of this correlation coefficient, enter your response here#, is greater/not greater than enter your response here#, the critical value for the female data set,no/a positive/a negativerelation exists between MRI count and IQ for females. Because the correlation coefficient for males is negative/positive and the absolute value of this correlation coefficient, enter your response here#, is greater/not greater than the critical value for the male data set, enter your response here#,no/a positive/a negative relation exists between MRI count and IQ for males. (Round to three decimal places as needed.) Critical value table 3 0.997 4 0.950 5 0.878 6 0.811 7 0.754 8 0.707 9 0.666 10 0.632 11 0.602 12 0.576 13 0.553 14 0.532 15 0.514 16 0.497 17 0.482 18 0.468 19 0.456 20 0.444 21 0.433 22 0.423 23 0.413 24 0.404 25 0.396 26 0.388 27 0.381 28 0.374…Consider the simple linear regression model Y = a +Bx + E for i = 1,2,...,n. The variances of two estimators i.e. V(@) and V(B) are defined as respectively Nanersite of ARm of Select one: and V(8) +2 (+ %3! V(a) = o? %3D v(a) = o? ; and V(B) = Syx o v(a) = o (:-mnd v(A) - and V(B) = o v(a) = o? (1 + and V(B): Syx = a4 o va) = (; +)md V(f) = and V(ß) Syy %3D Syr fs fo fa 24 & 5 7 V E R Y D T-
- Suppose 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=…Assume a multiple linear regression y = Bo + B1 a1+ B2x2 + e. Which statement(s) is(are) true about the variance inflation factors (VIFS) of the coefficient estimates b1 and b2 ? I. The VIF of b, is the same as the VIF of b2. II. VIF will likely be large if X2 is highly positively correlated with X1 II. VIF will likely be large if X2 is highly negatively correlated with X1 IV. VIF will likely be close to 1 if X1 and X2 are independent O l and IV 1, II, III and IV Il and III OIV only I onlySuppose a local university researcher wants to build a linear model that predicts the freshman year GPA of incoming students based on high school SAT scores. The researcher randomly selects a sample of 40 sophomore students at the university and gathers their freshman year GPA data and the high school SAT score reported on each of their college applications. He produces a scatterplot with SAT scores on the horizontal axis and GPA on the vertical axis. The data has a linear correlation coefficient of 0.481202. Additional sample statistics are summarized in the table below. Variable Sample Sample standard Variable description mean deviation high school SAT score x = 1495.716802 Sx = 109.915203 y freshman year GPA y = 3.260911 Sy 0.492802 r = 0.481202 slope = 0.002157 Determine the y-intercept, a, of the least-squares regression line for this data. Give your answer precise to at least four decimal places. a =
- Consider an AR(2) model: STEPS; -Write the equation of the AR(2) model- Determine the model based on the delay operator.- Find the expected value of the model- Find the variance of the model.- Find the covariances associated with 1,2, and s steps.- Find the associated correlation indices of 1,2, and s steps.- Assume that information is available up to time $h$ and the function that contains the accumulated information f(h, h-1,……), determine the forecasts and the error associated with 1,2, and s steps.- Assume that there is an initial value of the returns, denoted by $r_0$, and we have the AR(2) model at time rt, convert the AR(2) model into its equivalent MA(t)Consider an MA(2) model: STEPS -Write the equation of the M(2) model-Determine the model based on the delay operator.- Find the expected value of the model- Find the variance of the model.- Find the covariances associated with 1,2, and s steps.- Find the associated correlation indices of 1,2, and s steps.- Assume that information is available up to time $h$ and the function that contains the accumulated information f(h, h-1,……), determine the forecasts and the error associated with 1,2, and s steps.