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Lecture

Lect1 Stats 151.doc

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Department
Statistics
Course
STAT151
Professor
Sunil Barran
Semester
Winter

Description
Weiss, N Lecture Notes Lecture 1 What is statistics? Here, statistics is a group of methods used to collect, analyse, present, and interpret data and to make decisions. Example:census. WHY?? Descriptive: methods to view a given dataset. e.g. averages, histograms ,pie charts, bar graphs, mean , mode, median, standard deviation, variance…. Inferential: methods using sample results to infer conclusions about a larger population. e.g. 2 sample t-tests, simple linear regression Definitions: (i) A population consists of all elements whose characteristics are being studied. e.g. GPA of all Grant MacEwan students , Canadian Census (ii) A sample is a portion of the population selected for study. e.g. GPA of 10 Stats151 Grant MacEwan students in this section (iii) A representative sample is a sample that represents the characteristics of the population as close as possible. A random sample is a sample drawn in such a way that each element of the population has an equal chance of being selected. If chances are all the same  SRS(simple random sample) - e.g. A deck of cards: picking a red card is a simple random sample. Moreover, placing the card back in the deck is a sample with replacement and maintains SRS. Otherwise, there is sampling without replacement. ● an element/member of a sample or population is a specific subject or object about which information is collected ● a variable is a characteristic under study that assumes different values for different elements ● the value of a variable is called an observation ● a data set is a collection of observations on one or more variables Example: City Number of dog bites Center City 47 Elm Grove 32 Franklin 51 Bay City 44 Oakdale 12 Sand point 3 • Member: Each city included in the table • Variable: Number of dog bites reported • Measurement: Number of dog bites in a specific city • Data set: Collection of dog bite numbers for the six cities listed in the table. In an Observational study researchers simply observe characteristics and take measurements. Example: In a design of experiment, researchers impose treatments and controls and then observe characteristics and take measurements. Example: Sections 2.1-2.5 ● Quantitative variable: variable which can be measured numerically. Discrete variable: a quantitative variable whose possible values can be listed e.g.  Continuous variable: a quantitative variable whose possible value form some interval of numbers. e.g. ●Qualitative (categorical) variable: a nonnumerically valued variable. Ex: Histogram, Pie chart, bar graph, stem-and-leaf plots: A frequency distribution lists all categories and the # of elements that belong to each of the categories. Relative frequency of a category = Bar Graph : a graph representing the frequencies of respective categories Pie Chart: a circle divided into proportions representing the percentage relative frequencies Stem-and-leaf plot: To prepare a stem-and-leaf display for a data set, each value is divided into two parts; the first part is called the stem and the second part is called the leaf. The stems are written on the left side of a vertical line and the leaves for each stem are written on the right side of the vertical line next to the corresponding stem. EXAMPLE: the following data shows the method of payment by 16 customers in a supermarket checkout line(C=cash, CK=check, CC=credit card, D=debit, O=other): C CK CK C CC D O CK CC D CC C CK CK CC C Plots: MINITAB (bar graph): graphchart x(variable), Example 2.71: The number of patents a university receives is an indicator of the research level of the university. The number of patents awarded to a sample of 36 private and public universities was found to be: 93 27 11 30 9 30 35 20 9 35 24 19 14 29 11 2 55 15 35 2 15 4 16 79 16 22 49 3 69 23 18 41 11 7 34 16 Construct a stem-and-leaf plot for these data with: (a) one line per stem , (b) two lines per stem, (c) which do you find more useful? Why? Outliers: values that are very small or very large relative to the majority of the values in the data set. Dot plot: In order to prepare a dotplot, first we draw a horizontal line with numbers that cover the given data set. Then we place a dot above the value on the number line that represents each measurement in the data set Example: the following data give the number of times each of the 20 randomly selected male students from MacEwan ate at fast-food restaurants during a 7-day period: 5 8 10 3 5 5 10 7 2 1 10 4 5 0 10 1 2 8 3 5 Dotplot & Histogram: Distribution of a data set is a table, graph, or formula that provides the values of the observations and how often they occur. Shapes of distributions: Sections 3.1-3.4 Descriptive Measures Mean of a data set = (sum of all values) / (number of values) Notations: N = population size, n =sample size population mean= μ = (∑x)/N sample mean= x = (∑x)/n Median: middle value of a ranked/ordered data set Mode: the value that occurs with the highest frequency in a data set Remarks on mean, mode, median: i) if mean=median=mode data is symmetric ii) if mean > median data is right-skewed iii) if mean< median data is left-skewed ------------------------------------------------------------------------------------------ Example : The number of casinos in 11 states as of Dec.21, 2003 are for: CO IL IN IA LA MI MS MO NV NJ SD 44 9 10 13 18 3 29 11 256 12 38 i) Find the mean and median. ii) Do th
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