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Intro Stat Science & Health Sc
Dalhousie University
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MATH 1060 Lecture Notes - Lecture 1: Unimodality, Percentile

Quantitative data condition; that the data are values of quantitative variable whose units are known. Skewed ; if one tail stretches out farther than t

371

MATH 1060 Lecture 2: Measures of Variability

Ex- in choosing supplier a or supplier b, we might consider not only the mean delivery time but also the variability in delivery time for each. The dif

261

MATH 1060 Lecture 3: Choosing Measures of Variability/ Spread

Because x and s are not resistant (they are sensitive to extreme observations), they can be misleading when the data are strongly skewed or have outlie

263

MATH 1060 Lecture 4: Distribution Shape

Graph of the distribution of one quantitative variable. Most appropriate for large data sets, they give the ability to look at the shape of data. You c

358

MATH 1060 Lecture Notes - Lecture 5: Box Plot

Enable us to make quick visual comparisons between different data sets. Boxplot; the five number summary displayed on a graph. To tell whether it is sy

369

MATH 1060 Lecture Notes - Lecture 6: Unimodality

Left column; list each distinct value in the data set from least to greatest. Right column; count the number of times each value appears and record tho

250

MATH 1060 Lecture 7: Z-Tables

Chapter 5 - the standard deviation as a ruler and normal model. The total area under the overlaid bell shaped curve equals one. The smallest value in t

655

MATH 1060 Lecture 9: Probability Cont'd

When random sampling from a known population, one can use proportions from a large dataset as probabilities. Random; individual outcomes of the generat

752

MATH 1060 Lecture 10: Probability and Random Variables

Two events a and b are independent if p(a|b) = p(a) or p(a|b) = p(b) Random variable; a variable whose value is the result of carrying out some type of

479

MATH 1060 Lecture 11: Discrete Probability Models

Discrete random variable (drv): has a countable number of possible values. Continuous random variable, x: takes all values in an interval. There are an

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