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Types of Correlation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Positive correlationSlope is positive. Negative correlationSlope is negtive. No correlationSlope

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statistics for business and economics

Statistics for Business and Economics

Dr. TANG Yu

Department of Mathematics

Soochow University

May 28, 2007

slide2
Types of Correlation

Positivecorrelation

Slope is positive

Negativecorrelation

Slope is negtive

No correlation

Slope is zero

slide3
Hypothesis Test
  • For the simple linear regression model
  • If x and y are linearly related, we must have
  • We will use the sample data to test the following hypotheses about the parameter
sampling distribution
Sampling Distribution
  • Just as the sampling distribution of the sample mean, X-bar, depends on the the mean, standard deviation and shape of the X population, the sampling distributions of the β0-hat and β1-hat least squares estimators depend on the properties of the {Yj } sub-populations (j=1,…, n).
  • Given xj, the properties of the {Yj } sub-population are determined by the εj error/random variable.
model assumption
Model Assumption
  • As regards the probability distributions of εj (j =1,…, n),it is assumed that:
  • Each εj is normally distributed,

Yj is also normal;

  • Each εj has zero mean,

E(Yj) = β0 + β1 xj

  • Each εj has the same variance, σε2,

Var(Yj) =σε2 is also constant;

  • The errors are independent of each other,

{Yi} and {Yj}, i  j, are also independent;

  • The error does not depend on the independent variable(s).

The effects of X and ε on Y can be separated from each other.

graph show
Graph Show

Yi: N (β0+β1xi ;σ )

Yj: N (β0+β1xj ;σ )

xi

xj

The y distributions have the same shape at each x value

sum of squares
Sum of Squares

Sum of squares due to error (SSE)

Sum of squares due to regression (SSR)

Total sum of squares (SST)

example
Example

Total

anova table12
ANOVA Table
  • As F=35.93 > 6.61, where 6.61 is the critical value for F-distribution with degrees of freedom 1 and 5 (significant level takes .05), we reject H0, and conclude that the relationship between x and y is significant
slide13
Hypothesis Test
  • For the simple linear regression model
  • If x and y are linearly related, we must have
  • We will use the sample data to test the following hypotheses about the parameter
standard errors
Standard Errors

Standard error of estimate: the sample standard deviation ofε.

Replacing σε with its estimate, sε, the estimated standard errorofβ1-hat is

t test
t-test
  • Hypothesis
  • Test statistic

where t follows a t-distribution with n-2 degrees of freedom

reject rule
Reject Rule
  • This is a two-tailed test
  • Hypothesis
example17
Example

Total

calculation
Calculation

where 2.571 is the critical value for t-distribution with degree of freedom 5 (significant level takes .025), so we reject H0, and conclude that the relationship between x and y is significant

confidence interval
Confidence Interval

β1-hat is an estimator of β1

follows a t-distribution with n-2 degrees of freedom

The estimated standard errorofβ1-hat is

So the C% confidence interval estimatorsof β1 is

example21
Example

The 95% confidence interval estimatorsof β1 in the previous example is

i.e., from –12.87 to -5.15, which does not contain 0

regression equation
Regression Equation
  • It is believed that the longer one studied, the better one’s grade is. The final mark (Y) on study time (X) is supposed to follow the regression equation:
  • If the fit of the sample regression equation is satisfactory, it can be used to estimate its mean value or to predict the dependent variable.
estimate and predict
Estimate and Predict

Estimate

Predict

For the expected value of a Y sub-population.

For a particular element of a Y sub-population.

E.g.: What is the mean final mark of all those students who spent 30 hours on studying?

I.e., given x= 30, how large is E(y)?

E.g.: What is the final mark of Tom who spent 30 hours on studying?

I.e., given x= 30, how large is y?

what is the same
What Is the Same?

For a given X value, the point forecast (predict) of Y and the point estimator of the mean of the {Y} sub-population are the same:

Ex.1 Estimate the mean final mark of students who spent 30 hours on study.

Ex.2 Predict the final mark of Tom, when his study time is 30 hours.

what is the difference
What Is the Difference?

The interval prediction of Y and the interval estimation of the mean of the {Y} sub-population are different:

  • The prediction
  • The estimation

The prediction interval is wider than the confidence interval

example26
Example

Total

estimation and prediction
Estimation and Prediction
  • The point forecast (predict) of Y and the point estimator of the mean of the {Y} are the same:

For

estimation and prediction29
Estimation and Prediction
  • But for the interval estimation and prediction, it is different:

For

data needed
Data Needed

For

  • The prediction
  • The estimation
calculation31
Calculation

Estimation

Prediction

moving rule
The confidence interval

when xg =

The confidence interval

when xg =

The confidence interval

when xg =

Moving Rule
  • As xg moves away from x the interval becomes longer. That is, the shortest interval is found at x.
moving rule33
The confidence interval

when xg =

The confidence interval

when xg =

The confidence interval

when xg =

Moving Rule
  • As xg moves away from x the interval becomes longer. That is, the shortest interval is found at x.
interval estimation
Interval Estimation

Prediction

Estimation

residual analysis
Residual Analysis
  • Regression Residual– the difference between an observed y value and its corresponding predicted value
  • Properties of Regression Residual
    • The mean of the residuals equals zero
    • The standard deviation of the residuals is equal to the standard deviation of the fitted regression model
three situations
Three Situations

Good Pattern

Non-constant Variance

Model form not adequate

standardized residual
Standardized Residual
  • Standard deviation of the ith residual

where

  • Standardized residual for observation i
standardized residual42
Standardized Residual
  • The standardized residual plot can provide insight about the assumption that the error term has a normal distribution
  • If the assumption is satisfied, the distribution of the standardized residuals should appear to come from a standard normal probability distribution
  • It is expected to see approximately 95% of the standardized residuals between –2 and +2
influential observation45
Influential Observation

Influential observation

high leverage points
High Leverage Points
  • Leverage of observation
  • For example
contact information
Contact Information
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