# A4 Test Notes for end of Unit exam

I can't stress enough that you really need to do your own test notes. This is just an example, the one's I used, in the end of unit test.

By doing my own I learnt more about statistics and reinforced existing knowledge.

- Created by: Debra Wilson
- Created on: 22-10-12 21:24

First 593 words of the document:

Binomial Cross-Tabulation and Chi- One Sample T-test Pearson's r and Linear Paired Samples T-test Independent Samples T-

CV Square 2 CV's MV Regression 2 MV's CV and MV test. CV and MV

Comparing one CV proportion Relationship between 2 CV's. Comparing one MV mean Relationship between 2 Relationship between CV Relationship between CV

with another CV proportion We are more likely than you with a known MV mean. Has MV's. More of this means and MV. Our this is bigger and MV. Ours is bigger

are. this changed? more of that. than our that. than yours.

Repeated Measures Independent Groups

Advertising campaign says that Men and women and Concerned that number of Are sales effected by One brand of chocolate Do stickers help sales of

20% of all chocolate bars chocolate preference. nuts was insufficient in temperature? lasts longer than another. chocolate bars?

should contain winning tickets. One sample with 2 chocolate bars. One sample with 2 scores, 1 sample two scores. 2 samples. CV and MV

One sample, CV measurements, both CV (sex One Sample, MV MV Independent groups.

and pref.)

n = sample size 2(df) = Value s = Std. Dev. x = before mean, s = before

Std. Dev. t (df) = t, p = Sig.

p = exact sig. p = Asymp. Sig t (df) = t, p = Sig. Predicted DV value = and

(vertical intercept + slope) x x = after mean, s = after

Nuisance Variables and Confounding factors. Nuisance IV Std. Dev

Normality

variable is any variable (not the IV) that can cause changes

Pearson's r relationship Normal looking bell

in the DV. Situational controlled by experiment design and r2 = r x r xd = test table mean,

strength. curve and within 2

Subject based controlled by randomisation. pd = test table Std. Dev

=>75 strong std. dev of mean.

Confounding happens when variable can affect both DV (report sign with values)

.45 to .74 moderate

and IV. t (df) = t, p = Sig.

.25 to .44 weak

=<.24 extremely weak

z = (value mean) / Std. Dev. Error in Methodology units of analysis

z score = proportion 2

Pearson's r tells us how Causal Relationship this causes that

IV goes in columns, DV goes much of the variation in No outliers, sub-groups and Poorly Designed selection of samples

Percentage = Proportion x 100 in rows. the DV can be explained also must be linear Logic of experiment design of study

by the linear relationship

between the IV and the Normality Normality

n = sample number, 2 = (df) = t, p = sig. DV r = -P's cor., n = #, p = sig. ( = mean, s = std.dev) ( = mean1, s = std.dev1

p = sig. (report percentages of Mean (s = std.dev.) p = lower and upper before and after litres, n = N1)

interest) t(df) = t, p = sig. (report negative values) ( d = mean diff, sd = diff) more and less

mean diff. t(df) = t, p = sig. t(df) = t, p = sig

Hypothesis/Research Question Hypothesis/Research Question Hypoth./Research Question Hypoth./Research Question Hypoth./Research Question Hypoth./Res. Question

Sample desc and % result Sample desc and % result Sample desc & result (s = sd) Relationship desc Sample desc and stats Sample desc

Significance n = , p = Relationship significance X2 Mean diff. and significance t= Pearson's r (r =) significance (before and after ) (greater and less )

95% CI if significant 95% CI if significant 95% CI if significant Conclusion = Interpretation of Mean difference d Significance (t=)

Conclusion Conclusion Conclusion Slope. Significance (t=) Conclusion

Conclusion

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