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Slides for different statistics tests., Lecture notes of Statistics

Powerpoint slides for different statistics tests.

Typology: Lecture notes

2019/2020

Uploaded on 09/22/2020

himanshi-swaroop
himanshi-swaroop ๐Ÿ‡ฎ๐Ÿ‡ณ

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Tests for Mean & Variance
Tests
One Sample
One sample z test
One sample t test
Two
Samples
Two sample z test
Two sample t test
Paired t test
Two sample standard
deviation
More than 2
samples ANOVA
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Tests for Mean & Variance

Tests One Sample One sample z test One sample t test Two Samples Two sample z test Two sample t test Paired t test Two sample standard deviation More than 2 samples ANOVA

Errors of Statistical Tests

True State of Nature H 0 Is true H a Is true Conclusion Support H 0 / Reject H a Correct Conclusion Type II Error Support H a / Reject H 0 Type I Error Correct Conclusion (Power)

Significance Level

Level of Confidence / Confidence Interval: C = 0.90, 0.95, 0.99 (90%, 95%, 99%) Level of Significance: ฮฑ = 1 โ€“ C (0.10, 0.05, 0.01)

Power

โ– Power = 1 โ€“ ฮฒ (or 1 - type II error) โ– Type II Error: Failing to reject null hypothesis when null hypothesis is false. โ– Power: Likelihood of rejecting null hypothesis when null hypothesis is false. โ– Or: Power is the ability of a test to correctly reject the null hypothesis.

p Value

โ– p value is the lowest value of alpha for which the null hypothesis can be rejected. (Probability that the null hypothesis is correct) โ– If p = 0.01 you can reject the null hypothesis at ฮฑ = 0. โ– p is low the null must go / p is high the null fly.

Hypothesis Testing

  1. State the Alternate Hypothesis.
  2. State the Null Hypothesis.
  3. Select a probability of error level (alpha level). Generally 0.
  4. Select and compute the test statistic (e.g t or z score)
  5. Critical test statistic
  6. Interpret the results.

Hypothesis Testing

โ– Two Tail Tests โ– H 0 : ฮผ = 150cc โ– H a : ฮผ โ‰  150cc

Calculate Test Statistic

โ– Single sample โ– z = (x- ฮผ ) / ฯƒ โ– Mean of Multiple samples โ– z = (xฬ„ - ฮผ) / ( ฯƒ / โˆšn)

One Sample t Test

t critical = 3.

One Sample t Test

โ– Calculated value โ– t = [xฬ„ - ฮผ ] / [s / sqrt( n ) ] โ– Example: Perfume bottle producing 150 cc, 4 bottles are randomly picked and the average volume was found to be be 151 cc and sd of sample was 2 cc. Has mean volume changed? ( 95 % confidence) โ– t cal = ( 151 - 150 )/[ 2 / sqrt( 4 ) ] = 1 / 1 = 1 โ– t critical = 3.182 > Fail to reject Ho

One Sample Chi Square

โ– For testing the population variance against a specified value ฯƒ

One Sample Chi Square

โ– Example: A sample of 25 bottles was selected. The variance of these 25 bottles as 5 cc. Has it increased from established 4 cc? 95% confidence level. โ– X 2 = 24x5 / 4 = 30 โ– What is critical value of Chi Square for 24 degrees of freedom?

One Sample Chi Square

โ– Example: A sample of 25 bottles was selected. The variance of these 25 bottles as 5 cc. Has it increased from established 4 cc? 95% confidence level. โ– X 2 = 24x5 / 4 = 30 โ– Critical value of Chi Square for 24 degrees of freedom = 36. โ– Fail to reject H 0

Tests for M ean & Varianc e

Tests One Sample One sample z test One sample t test Two Samples Two sample z test Two sample t test Paired t test Two sample standard deviation More than 2 samples ANOVA