See the attached image for the introduction. What are two-sided 95% confidence limits for the increase in mean selling price associated with each additional square foot? (Plugin completely, but you need not simplify.) JMP output appears below for simple linear regression with data from the price, y (in $1000), ofn = 28 Seattle home prices. The explanatory variable is the total number of square feet in thehome.- Response Price ($000)Regression Plot600500400300200- Distributions100015002000250030003500Square FeetSquare FeetSummary of FitRSquareRSquare AdiRoot Mean Square ErrorMean of ResponseObservations (or Sum Wgts)0.5605030.543599356.821428Analysis of VarianceSum of100015002000250030003500SourceDFF RatioSquares Mean Square1 249200.64Model249201 33.1585- Summary StatisticsError26Prob > FC. Total27<.0001Mean1923.1071Std Dev653.11574v Parameter EstimatesStd Er Mean123.42727TermEstimate Std Error t Ratio Prob>ltUpper 95% Mean 2176.359Lower 95% Mean 1669.8553InterceptSquare Feet 0.1470966 0.02554573.938964 51.785541.43 0.16535.76 <.000128(ooos) eoad

Name: See the attached image for the introduction. What are two-sided 95% co
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Description: See the attached image for the introduction. What are two-sided 95% confidence limits for the increase in mean selling price associated with each additional square foot? (Plugin completely, but you need not simplify.) JMP output appears below for sim

Confidence Interval: Confidence interval is the interval estimate of the population parameter. It is…

Answered: JMP output appears below for simple…

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Author:Amos Gilat

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See the attached image for the introduction.

What are two-sided 95% confidence limits for the increase in mean selling price associated with each additional square foot? (Plugin completely, but you need not simplify.)

JMP output appears below for simple linear regression with data from the price, y (in $1000), of
n = 28 Seattle home prices. The explanatory variable is the total number of square feet in the
home.
- Response Price ($000)
Regression Plot
600
500
400
300
200
- Distributions
1000
1500
2000
2500
3000
3500
Square Feet
Square Feet
Summary of Fit
RSquare
RSquare Adi
Root Mean Square Error
Mean of Response
Observations (or Sum Wgts)
0.560503
0.543599
356.8214
28
Analysis of Variance
Sum of
1000
1500
2000
2500
3000
3500
Source
DF
F Ratio
Squares Mean Square
1 249200.64
Model
249201 33.1585
- Summary Statistics
Error
26
Prob > F
C. Total
27
<.0001
Mean
1923.1071
Std Dev
653.11574
v Parameter Estimates
Std Er Mean
123.42727
Term
Estimate Std Error t Ratio Prob>lt
Upper 95% Mean 2176.359
Lower 95% Mean 1669.8553
Intercept
Square Feet 0.1470966 0.025545
73.938964 51.78554
1.43 0.1653
5.76 <.0001
28
(ooos) eoad

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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