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M 140 Test 1 B Name__________________ SHOW
M 140 Test 1 B Name__________________ SHOW

... 10. Consider the following data set: 2 3 5 7 8 10 12 15 20 31. According to the 1.5(IQR) rule, a) only 2 is an outlier. b) only 31 is an outlier. c) both 2 and 31 are outliers. d. there are no outliers. ...
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... Essentially, the t-value is the difference between group means (mean difference), divided by the variability of the two groups (standard error of difference). ...
Lecture 2: Descriptive Statistics and Exploratory Data Analysis
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... Before making inferences from data it is essential to examine all your variables. Why? To listen to the data: - to catch mistakes - to see patterns in the data - to find violations of statistical assumptions - to generate hypotheses …and because if you don’t, you will have trouble later ...
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... The variance is the average squared difference of an individual value from the population mean. Why squared? Example cases: Suppose we want to evaluate two GPS receivers for their accuracy. We take the receivers out to measure the distance between two points that are exactly 100.00 meters. We take 5 ...
H1: The Art and Science of Learning from Data
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... The population is the total set of subjects in which we are interested. A sample is the subset of the population for whom we have (or plan to have) data. Descriptive statistics refers to methods for summarizing the data. The summaries usually consist of graphs and numbers such as averages and percen ...
chapter 2
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... Residual plot – a scatterplot of the regression residuals against the explanatory variable. - It helps us to assess the fit of the regression line. - if plot unstructured and centered about 0, no major problem - if plot has a curve then a straight line is not the best fit of the data - if the residu ...
The Practice of Statistics
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Chapter 11
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Chapter 34 - Routledge
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... Statistical significance is a function of the co-efficient and the sample size: – the smaller the sample, the larger the co-efficient has to be in order to obtain statistical significance; – the larger the sample, the smaller the co-efficient can be in order to obtain statistical signifiance; – Stat ...
Ch 12: Analysis of Quantitative Data
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... graph) accounts for somewhere around 68 percent of the people in this group. Two standard deviations away from the mean (the red and green areas) account for roughly 95 percent of the people. And three standard deviations (the red, green and blue areas) account for about 99 percent of the people. • ...
Chapter 1 - Graphical and Numerical Summaries
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STAB22 Statistics I Lecture 3 1
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Statistics - Simmons College
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... distributed population or that both sets of scores were drawn from populations with the same variance or spread of scores Nonparametric methods do not make assumptions about the shape of the population distribution. These are typically less powerful and often need large samples ...
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... bivariate data. For example, the heights and weights of a group of people could be displayed on a scatter plot. If you are looking for values that fall within the range of values plotted on the scatter plot, you are interpolating. If you are looking for values that fall beyond the range of those val ...
Location of Packet
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... Criterion for identifying possible outliers using zscores: If observation more than 2 or 3 standard devations from the mean, obs. classified as a potential outlier. [ how much criterion] 1.5*IQR Criterion: If an observation above Q3+1.5*IQR or below Q1-1.5*IQR, then obs classified as a potential out ...
Ethan Frome - Hope College Math Department
Ethan Frome - Hope College Math Department

... were available in a bowl, and the investigators compared the number of crackers eaten by children watching the different kinds of movies. One kind was shown at 8 A.M. and another at 11 A.M. It was found that during the movie shown at 11 A.M., more crackers were eaten than during the movie shown at 8 ...
Document
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... Evaluate test statistics Use a test statistic to formulate a business decisions using regression analysis ...
ap statistics midterm review sheet
ap statistics midterm review sheet

... You will be given the information you need—you just need to plug the given information into the formulas above (in the order above). We will review this further in class… Interpret r and R2 (remember, if given R2 only, r can be positive or negative…r will have the same sign as the slope.) Create a s ...
Quantitative Measures - University of Oxford
Quantitative Measures - University of Oxford

... • Please put your name somewhere on the page • Paste figures into the doc-file (or rtf-file) and only submit the .sav-files • Name the x- and y-axis of the figures and give them a title • Do not work with var0001 (name and label varia ...
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...  If number of observation is odd, the median is the middle number  If number of observation is even, the median is the average of the two middle numbers C) Mode  The value which is most frequent.it represents the most common response.  Used for either numerical or categorical data  There may be ...
HypTestExam
HypTestExam

... a. determine how close a sample comes to the null hypothesis b. us to understand how one variable, X, relates to another variable, Y c. determine if a systematic association exists between two variables d. determine the shape of the empirical distribution of the variable (d, 57. The value obtained b ...
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Categorical variable

In statistics, a categorical variable is a variable that can take on one of a limited, and usually fixed, number of possible values, thus assigning each individual to a particular group or ""category."" In computer science and some branches of mathematics, categorical variables are referred to as enumerations or enumerated types. Commonly (though not in this article), each of the possible values of a categorical variable is referred to as a level. The probability distribution associated with a random categorical variable is called a categorical distribution.Categorical data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data. More specifically, categorical data may derive from either or both of observations made of qualitative data, where the observations are summarised as counts or cross tabulations, or of quantitative data, where observations might be directly observed counts of events happening or might be counts of values that occur within given intervals. Often, purely categorical data are summarised in the form of a contingency table. However, particularly when considering data analysis, it is common to use the term ""categorical data"" to apply to data sets that, while containing some categorical variables, may also contain non-categorical variables.A categorical variable that can take on exactly two values is termed a binary variable or dichotomous variable; an important special case is the Bernoulli variable. Categorical variables with more than two possible values are called polytomous variables; variables are often assumed to be polytomous unless otherwise specified. Discretization is treating continuous data as if it were categorical. Dichotomization is treating continuous data or polytomous variables as if they were binary variables. Regression analysis often treats category membership as a quantitative dummy variable.
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