Statistics Review

ABOUT THIS CONTENT

The primary concepts from a core MBA statistics class.
Subject: Statistics

Random variable – variable that takes on numerical values determined by the outcome of a random experiment. Usually denoted by X
Probability distribution – expresses probability that a continuous random variable takes on value X=x

  • Adding a constant to a variable shifts the distribution
  • Multiplying by a constant changes the dispersion of a variable (if <1σ reduces, if >1σ increases)

To compute the probability for a normal random variable, convert X~N(μ, σ2) to Z~N(μ, σ2):

Point Prediction – If data is independent, the best prediction is the mean. If data is not independent (runs test significant at <0.05) then Xt+1 = Xt + μchange + εt

Runs Test

  • Few runs -> cyclical time series
  • Many runs -> oscillating time series
  • Medium # of runs -> independent time series
  • When we predict for independent series (=μ of series) the confidence interval is a multiple of σ
  • When we predict for non-independent time series (=μchange + εt) the confidence interval is a multiple of the variance (σ2) {e.g. X70 ~ N(X68 + 2μchange , 2σchange2)}

A random walk exists is the changes from period to period are independent and normally distributed

Estimating μ and σ {X representative of μ, s2 representative of σ2)

  • If X ~ N(μ, σ2) then X  ~ N(μ, σ2/n)

Linear Regression (α + ßx) + εt with εt iid N(μ, σε2)
Make distance from each point to the estimated line as small as possible. Minimize Σεt2

4 Basic Assumptions and how to check them:

  • linearity – non-linear pattern in x/y plot; u-shaped pattern in the Residuals vs. Fit plot
  • constant variance – look for funnel in the Residuals vs. Fit plot
  • independence – pattern in time series plot; runs test
  • normality – histogram of e’s (residual = e)

Assuming X is independent (data set) implies the e’s are independent. Must check this.

Specification bias – exists when an explanatory variable that should be included in regression is left out. {if a coefficient doesn’t make sense it could be due to specification bias}

Understanding the Regression Output:

In regressions, error terms don’t accumulate (predictions)
Coefficients are random variables because they depend on sample of points
Any time you standardize using an estimate you get a t-distribution not a z-distribution



  • Dummy variables are useful when the # of data points isn’t sufficient for multiple single regressions
  • Interpret regression model using dummy variables as multiple parallel regression lines where the coefficients are the distance between the lines
  • Baseline is the dummy variable that is left out (intentionally) when running the regression
  • Best prediction of true regression α +  ßx = ε is a + bx (ε=0)

Confidence Intervals
2 components of prediction error:

  1. (α +  ßx) – (a + bx)
  2. ε
    • the ore uncertainty about the 2 sources of error, the more uncertain the prediction is and therefore a wider confidence interval
    • Minitab gives a 95% interval

Time Series with Trend
Pattern consists of:

  • Trend – model with Time variable
  • Seasonal component – model with multiple dummy variables
  • Short-term correlation (cyclical) – model w/ Yt-1
  • Unpredictable component (ε)

To modify the model to account for non-constant variance, run a regression model for % change in variable (i.e. sales) {this is like a non-independent time-series point prediction}

Leading indicators can improve the model BUT are not useful for predicting

It is important to know how close b is to ß:
The sampling distribution for b is b ~ N(ß, σb2)     

Hypothesis Testing:  (H0: ß=0  HA: ß<>0) – useful for determining which variables have explanatory power
Type I Error – say ß=1 when ß=0 (reject H0 when H0 is true)
Type II Error – say ß=0 when ß=1 (accept H0 when H0 is false)

Procedure:

  1. Collect sample
  2. Compute b
  3. Compare to critical t-value
  4. Reject H0 if (b/sb) > tcritical

We’re choosing a cutoff value such that pr(Type I error) = 0.05 (α)
Each coefficient has a t-distribution with (n – total # coefficients) degrees of freedom (tn-#, α)

  • If one-sided decision (i.e. HA: ß>0), use α as given percentage
  • If two-sided decision (i.e. HA: ß>0), use α as ½ of the given percentage
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