9. Confidence interval for mean value of Y given x

10. Prediction interval for a randomly chosen value of Y given x

11. Coeff. of Correlation

12. Adjusted R2

13. Variance Inflation Factor

14. Beta Weights

15. Partial F Test

SSER - sum of squares of error of reduced model SSEF - sum of squares of error of full model r – no. of variables dropped from full model.

16. Outliers
Measure | Potential Outliers |
Standardized residual, Studentized residual | > 3 (3 sigma level) | Mahalanobis distance | > Critical chi-square value with df = number of explanatory variables(Outliers in independent variable) | Cook’s distance | > 1 implies potential outlier |

Leverage values | > 2(k+1)/n, then the point is influential (k is the number of independent variables and n is the sample size) | SDFBeta | > 2/n |
SDFFit | |
17. Mahalanobis Distance
Mi = (Xi – X)2/ Sx
18. Cook’s Distance
Di =
∑j (Yj – Yj(i))2/k x MSE
19. Durbin Watson Test
Durbin Watson value close to 2 implies no auto-correlation
Durbin Watson value close to 0 implies positive auto-correlation Durbin Watson value close to 4 implies negative auto-correlation 20. Relationship between F and R2
F = (R2/1- R2) x ((n-(k+1))/k)
FORECASTING
1. Exponential Smoothing

2. Double Exponential Smoothing

3. Theil’s Coeff

U1 is bounded between 0 and 1, with values closer to zero indicating greater accuracy. If U2 = 1, there is no difference between naïve forecast and the forecasting technique If U2 < 1, the technique is better than naïve forecast

...H0: u1 = u2 vs. HA: u1 ≠ u2 (this is two tailed ) also possible: * H0:uWomen≥ uMen vs. HA: uWomen0 OR d 6000 → Ha: p > 0,5 of Ha≠0,5 indien dubbelzijdig wilt testen.notice: one-sided testing!! |
15.4.2 The Multiple Regression Model 13- First compute the new variable Price Difference: Transform, Compute Variable - In the compute variable box edit PriceDif as Target...

...Helpful Advising Information
College of Sciences
October 16, 2012
I. General Education Requirements
A. “C” or better in ENGL 1158 (1159 for Honors students); “D” ok in ENGL 1157.
B. Literature can be any language; writing courses do not count.
C. Social Sciences – 6 hours, including 3 hours at 2000+ level.
1. Includes Anthropology, Economics, Geography, Political Science, Psychology,
Sociology, and Urban Studies.
2. History is NOT Social Science; it is Humanities.
D....

...Homework 2
Solution Key
Problem 1. Suppose that you sell short 500 shares of Intel, currently selling for $40 per share,
and you give your broker $15,000 to establish your margin account. Assume Intel pays no
dividends.
a) If you earn no interest on the funds in your margin account, what will be your rate of
return after one year if Intel stock is selling at (i) $44; (ii) $40; (iii) $36?
The gain or loss on the short position is 500 P . Invested funds are $15,000.
Therefore, your...

...StatisticsFormulaSheet
Numerical Descriptive Measures
1. Population Variance = σ 2. Sample Variance = s
2 =
2
=
∑iN 1 ( x i − μ ) =
2
N n ( x − x) 2 ∑i =1 i
n −1
3. Inter‐quartile Range = Q 3 − Q1
Expectation and variance 1. Expected value of X: μ = E ( X ) = ∑ xp ( x )
2. Variance of X: σ
2 2 = ∑ ( x − μ ) p( x)
Probability 1. Additive Rule: P ( A ∪ B ) = P ( A) + P ( B ) − P ( A ∩ B...

...Statistics
ANOVA & Least Squares
Tyrone Sewell
Statistics, MAT 201, Module V-CA5
Alfred Basta
December 20, 2009
Statistics
ANOVA & Least Squares
Look at the data below for the income levels and prices paid for cars for ten people:
| Annual Income Level |Amount Spent on Car |
|38,000 |12,000 |
|40,000 |16,000 |
|117,000 |41,000...

...Linear Regression deals with the numerical measures to express the relationship between two variables. Relationships between variables can either be strong or weak or even direct or inverse. A few examples may be the amount McDonald’s spends on advertising per month and the amount of total sales in a month. Additionally the amount of study time one puts toward this statistics in comparison to the grades they receive may be analyzed using the...

...squared deviation from the population mean, as defined by the following formula:
σ2 = Σ ( Xi - μ )2 / N
where σ2 is the population variance, μ is the population mean, Xi is the ith element from the population, and N is the number of elements in the population.
Observations from a simple random sample can be used to estimate the variance of a population. For this purpose, sample variance is defined by slightly different formula, and uses a slightly different...