ADM 2304 Study Guide - Midterm Guide: Bmw 8 Series, Minitab, Complement Factor B

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ADM 2304 M Jaclyn Ebert 6221545
One Sample T-Test One Population Mean
1. 
 Left-Tail Z-Test
 Right-Tail Z-Test
 Two-Tail Z-Test
Assumptions: random sample, normally distributed
If sample is >10% of population, ×denominator by: 

2.  
3. 

 

Find in t-Table
4. Since  , reject
There is sufficient evidence to show
Since  , do not reject
There is insufficient evidence to show
5. Symmetrical CI for Two-Tailed:


Upper bound for Left-Tailed: +
Lower bound for Right-Tailed: = (-
6. p-val = 
Find  in middle of t-Table Subtract from 1 for Right T
Since {p-val = } < {}, reject
Since {p-val = } > {}, do not reject
One Sample Z-Test One Population Mean
Same as T-Test. Used when population SD is known.

 

LS = α One Tail
0.001 3.0902 0.0005 3.2905
0.005 2.5758 0.0025 2.8070
0.01 2.3263 0.005 2.5758
0.02 2.0537 0.01 2.3263
0.05 1.6449 0.025 1.9600
0.1 1.2816 0.05 1.6449
0.2 0.8416 0.1 1.2816
Zcrit Two Tailed
CI Zcrit
90% 1.6449
95% 1.9600
98% 2.3263
99% 2.5758
One Proportion Z-Test
 # = population % =
 Left-Tail Z-Test for decrease
 Right-Tail Z-Test for increase
 Two-Tail Z-Test for different

Assumption: 



 





Interpretation: 95% of the confidence intervals contain the
true population mean.
Binomial Test


Assumption: 


n = sample size/# trials, x = number of successes, n-x =
number of failures, p = probability of success, q = (1-p) =
probability of failure.
Testing for Change:
Test
Two-Tailed
Right Tailed
Left Tailed
For
More?
Less?
CI
Asymmetric
Lower Bound
Asymmetric
Upper Bound
CI & HT
Consistent


Normality: Outliers?
Test mean with normal distribution Parametric Test
Test median for non-normal distribution Non-Parametric
Difference of the Mean T-Test Two Sample/Pop Means

 Two-Tailed T-Test
Assumptions: samples independent & normally distributed
Unequal Population Variance:
 



 


Equal Population Variance with pooling:
 

 


There is (in)sufficient evidence to show that there is a
difference in the mean between the two samples.
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