Class Notes for Dwayne Pare

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UTSCPSYB07H3Dwayne PareFall

Assignment-1.pdf

OC590921 Page
14 Apr 2014
98
A statistics instructor was interested in investigating the time of day his lectures were being held on students test scores. He selected a sample of 1
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 5: Pretz, Venn Diagram, Conditional Probability

OC34043317 Page
30 Nov 2015
26
Probability can be expressed in 0-1 or 0%-100% P(event)=0 means event never occurred, p(event)=0. 5 (50% event occurred), p(event) 1 (100% event occurr
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 3: Kurtosis, Normal Distribution, Mode 9

OC34043319 Page
30 Nov 2015
19
Bell curve: an average in which the majority achieves (normalizing the data), data can have two extremes presented in graph so bell curve gives the bal
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 3: Quartile, Interquartile Range, Variance

OC34662422 Page
9 Oct 2017
0
Psyb07 descriptive statistics lecture 3, tutorial 2, chapter 2 (pg. Normal distribution/bell curve: single peak, symmetrical, can add a constant ex. ad
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 2: Histogram, Frequency Distribution, Bar Chart

OC3466248 Page
9 Oct 2017
0
Psyb07 lecture 2, tutorial 1 + chapter 2 (pg. Bedmas: brackets, exponents, division & multiplication, addition & subtraction. Population: entire collec
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 3: Kurtosis, Bar Chart, Skewness

OC10621229 Page
27 Sep 2016
1
Basic concepts: population vs sample, random sampling & external validity, random assignment & internal validity. Variable: independent vs dependent, d
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 4: Conditional Probability, Mutual Exclusivity, Odds Ratio

OC3466249 Page
12 Oct 2017
0
Empirical view: toss a (cid:272)oi(cid:374) re(cid:272)ord the out(cid:272)o(cid:373)es, 10 times: 7 heads 0. 7, 20 times: 12 heads 0. 6, 100 times: 55
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 4: Interquartile Range, Statistical Inference, Quartile

OC106212212 Page
29 Sep 2016
8
Expected value of = : from an individual sample may not = (of a population) sample may not equal population, if i average the from many samples, the av
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 4: Quartile, Variance, European Route E6

OC34043316 Page
30 Nov 2015
16
Sample mean is unbiased estimator of population mean (the expectation of getting the same mean for sample and population) The larger the sample size th
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture 11: lecture 11 Correlation and Regression

OC13818543 Page
30 Nov 2017
0
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 6: Unimodality, Sampling Distribution, Conditional Probability

OC34043315 Page
30 Nov 2015
29
Successful probability of them happening doesn"t have to be equal (coin toss 50/50) or multiple choice (4 options) Order doesn"t matter p(5 heads out o
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UTSCPSYB07H3Dwayne PareFall

PSYB07H3 Lecture Notes - Lecture 2: Statistical Inference, Barbecue, Descriptive Statistics

OC3404334 Page
30 Nov 2015
22
Order of operations (bedmas) [left to right] Entire collection of event in which you"re interested. Example: population= psyb07 class, sample= psyb07 t
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