# Summary of STAT193_VUW.pdf - Map of STAT193...

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Map of STAT193 “Statistics in Practice” Section Weeks Title …contains A 1 - 3 Informal inference with 1 and 2 variables; probability Critical thinking; variables; data display – bar charts, boxplots, histograms, scatterplots; summary statistics; probability definitions and rules B 4 - 7 Formal inference: hypothesis testing with 1 variable Binomial and Normal probability distributions; estimation; sign test; t-test for a mean; Z-test for a mean; Central Limit Theorem C 8 - 11 Formal inference: hypothesis testing using 2 variables Chi-square test; ANOVA; t-test for 2 independent means; linear regression D 12 Consolidation Revision and exam techniques, past exam questions, linking topics 1
Week Lecture Title Section A (Weeks 1 – 3) Informal inference with 1 and 2 variables; probability 1 1 Introduction to Statistics 1 2 Data; Sampling from populations; Administration 1 3 Background maths; Types of variable; One variable : categorical data and graphs 2 1 Informal inference from categorical graphs; Numerical data and graphs; Informal inference from numerical graphs 2 2 More numerical graphs, percentiles; Mean and standard deviation 2 3 Two variables – bivariate data 3 1 Exercises on bivariate graphs; Misleading statistics 3 2 Probability fundamentals 3 3 Conditional probability; Random variables 2
Week Lecture Title Section B (Weeks 4 – 7) Formal inference: hypothesis testing with 1 variable 4 1 Binomial distribution 4 2 Normal distribution 4 3 Inverse normal; standard normal distribution; standardising scores 5 1 Hypothesis testing procedure; Sign Test 5 2 Sign Test 5 3 Sign Test for paired differences 6 1 Introduction to t -distributions; Estimation; Confidence intervals 6 2 t -test for mean 6 3 t -test of a mean using iNZight; extension work on confidence intervals 7 1 t -test for paired differences; Type I and II errors 7 2 Distribution of the sample mean; Central Limit Theorem 7 3 Sample means with the CLT 3
Week Lecture Title Section C (Weeks 8 – 11) Formal inference: hypothesis testing with 2 variables 8 1 Analysing two variables; chi-square test 8 2 Chi-square test II; causation 8 3 Big picture! Guest lecturers 9 1 ANOVA 9 2 ANOVA; confidence intervals 9 3 ANOVA assumptions and residuals 10 1 t -test for difference of two means 10 2 Correlation and linear regression 10 3 Linear regression; prediction 11 1 Linear regression: residuals 11 2 Linear regression: assumptions 11 3 Testing gradient of regression line 4
Section A (Weeks 1 – 3) Informal inference with 1 and 2 variables; probability 5
6 Background maths you need 1. Percentages ↔ decimals (proportions) 2. Fractions ; ଵ଴଴ 3. Number line (for negative nos. esp.) 4. , signs 5. Scientific notation ିସ 6. Rounding 7. Substitution in formulae 8. Plotting points on graph (scatterplot) 9. Basic (sigma) notation If ݔ = 3, ݔ = 10, ݔ = 1, ݔ = 2 then ∑ ݔ = ௜ୀଷ ௜ୀଵ 3 + 10 + 1 = 14
Introduction A key idea behind Statistics is to make objective inference in the presence of uncertainty we use a sample to make inference about the population it comes from selection process of a sample is very important – aim for