I need some answering these questions on T-tests and Regressions:
1) You are estimating the cost of optical sensors based on the POWER OUTPUT of the sensor. You
decide to calculate the coefficient of determination (R2) as part of determining the goodness of fit of
an equation. Using the preliminary calculations below, calculate the R2 and determine its meaning.
a. 4.67% of the variation in the cost is being explained by the power.
b. 95.33% of the variation in the power is being explained by the cost.
c. 95.33% of the variation in the cost is being explained by the power.
d. 4.67% of the variation in the power is being explained by the cost.
2) You are estimating the cost ($K) of optical sensors based on the radius of the sensors. Using the
preliminary calculations from a data set of 8 sensors, determine the equation of the line. (Round your
intermediate calculations to 3 decimal places) SY = 2575 SX=21 SXY=9105 SX2=102
a. Cost = - 63.277 + 111.897 (Radius)
b. Cost = 50.040 + 190.520 (Radius)
c. Cost = 1.908 + 307.936 (Radius)
d. Cost = 190.520 + 50.040 (Radius)
3) A coworker is considering the use of a log linear (power) model using weight to estimate the cost
of a manufacturing effort. They have performed the following calculations in LOG SPACE using
natural logarithms. Select the corresponding UNIT SPACE form of this power model equation.
Log Space b1 = 1.455294 b0 = 3.673610
a. Cost = 39.393861 (Weight)1.455294
b. Cost = 1.455294 + 3.939388 (Weight)
c. Cost = 3.939388 + 1.455294 (Weight)
d. Cost = 51.387143 (Weight)1.455294
4) You are estimating the manufacturing hours for an airframe based on the airframe weight. The
airframe you are estimating weighs 141784 pounds. Given the following equation, select the correct
response from each pair. Hours = 124.50 + 0.75 (Weight in pounds)
- The independent variable is Hours
- The independent variable is Weight
- The slope is 124.50
- The slope is 0.75DAU
- The estimated manufacturing hours for your airframe are 762 hours
- The estimated manufacturing hours for your airframe are 106462.5 hours
5) You have calculated the following POWER model and associated UNIT SPACE values:
a. Power equation because it has a lower standard error than the linear model.
b. Linear equation because it has a higher standard error than the power model.
c. Linear equation because it has a lower standard error than the power model.
d. Power equation because it has a higher standard error than the linear model.
6) Given a one independent variable linear equation that states cost in $K, and given the following
information, calculate the STANDARD ERROR and determine its meaning.
a. If we used this equation, we could typically expect to be off by ± 42.83%.
b. If we used this equation, we could typically expect to be off by ± $42.83K.
c. If we used this equation, we could typically expect to be off by ± 37.09%.
d. If we used this equation, we could typically expect to be off by ± $37.09K.
7) You are trying to determine the statistical significance of an equation. Given the following
information, test the slope of the equation at the 90% level of confidence. Select the correct answer
out of each pair of choices. Cost = - 76.25 + 114.82 (Range) n=9 Sb1=17.669
- The tp is 1.895
- The tp is 2.365
- The tc is 4.315
- The tc is 6.498
We would REJECT the null hypothesis
We would FAIL TO REJECT the null hypothesis
We would consider using the equation
1. R^2 is calculated as regression sums of squares / total sums of squares. Thus,
R^2 = 6874/147172 = 4.67%
Our model is trying to explain the cost of optical sensors, so the answer is:
(A) 4.67% of the variation in the cost is being explained by the power.
2. The slope b can be calculated by
b = (Sxy - 1/8*Sx*Sy)/(Sx^2-1/8*(Sx)^2) = 50.04
The intercept a is given by:
a = 1/8(Sy-b*Sx) = 190.52
(D) Cost = 190.520 + ...
Full solution with explanations are provided.
Statistics: T-tests and ANOVAs
Hello, I need help answering the following questions:
1. Please provide enough information per each question.
2. You can provide a chart or graph along with a question also for better understanding.
Please see attachment.
Thank you, your help is needed.
1. Describe the rejection region for a two-tailed Z test when alpha = .01
2. Describe the rejection region for a one-tailed t-test when alpha = .05, n=10, and the alternative hypothesis is Ha: u < 0.
3. When would you use a paired sample t-test?
4. When would you do a non-parametric hypothesis test?
5. What is the chi-square goodness of fit test? (Week 3)
6. What are the assumptions for ANOVA, the analysis of variance?
7. Define Correlation Coefficient.
8. What are the regression analysis assumptions?
9. Compare correlation and regression.
10. Define residual.View Full Posting Details