3.5 Exploratory Data Analysis
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This document discusses exploratory data analysis techniques including boxplots and five-number summaries. It explains how to organize and graph data using histograms, frequency polygons, stem-and-leaf plots, and box-and-whisker plots. The five important values used in a boxplot are the minimum, first quartile, median, third quartile, and maximum. An example constructs a boxplot for a stockbroker's daily client numbers over 11 days.
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1.5 Observational vs. Experimentalmlong24 This document explains the key differences between observational and experimental studies. Observational studies involve observing phenomena as they occur naturally, while experimental studies involve manipulating variables and determining their effects. It defines related concepts like independent and dependent variables, treatment and control groups. It notes advantages of experimental studies like controlling subject selection and variable manipulation, and disadvantages like artificial settings and potential confounding variables.
1 3 Variables and Types of Data
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1.1-1.2 Descriptive and Inferential Statistics
1.1-1.2 Descriptive and Inferential Statisticsmlong24 Statistics involves collecting, organizing, and analyzing data to draw conclusions. Descriptive statistics summarize and describe data, while inferential statistics generalize from samples to populations through estimation, hypothesis testing, and prediction. The chapter introduces key concepts including variables, data, random variables, data sets, data values, populations, samples, descriptive statistics, probability, and inferential statistics. Students are assigned problems applying these statistical concepts.
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2.4 Other Types of Graphs
2.4 Other Types of Graphsmlong24 This document discusses different types of graphs used to represent data including Pareto charts, time series graphs, and pie charts. It provides details on how to construct each graph type, including arranging bars from largest to smallest frequency for a Pareto chart and dividing a circle into wedges according to category percentages for a pie chart. Examples and steps are given for drawing each graph. Additional topics covered include stem and leaf plots and comparing data sets using different graph types.
2.1-2.2 Organizing Data
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3.3 Measures of Variation
3.3 Measures of Variationmlong24 This document discusses measures of variation in data, including range, variance, and standard deviation. It provides examples of calculating these measures for both individual data points and grouped data. The key measures are:
- Range is the highest value minus the lowest value.
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- Standard deviation is the square root of the variance, measuring average deviation from the mean.
- Coefficient of variation allows comparison of variables with different units by expressing standard deviation as a percentage of the mean.
- Chebyshev's theorem and the empirical rule specify what proportion of data falls within a given number of standard deviations of the mean.
3.4 Measures of Position
3.4 Measures of Positionmlong24 This document discusses various measures of position such as percentiles, quartiles, and standard scores. It provides examples of how to calculate percentiles, quartiles, z-scores, and identify outliers. Key points covered include how to interpret percentiles and z-scores, how to calculate quartiles and find values corresponding to specific percentiles, and the procedure for identifying outliers in a data set.
3.1-3.2 Measures of Central Tendency
3.1-3.2 Measures of Central Tendencymlong24 This document discusses measures of central tendency including the mean, median, and mode. It provides examples of calculating the mean from raw data sets and frequency distributions. The median and mode are defined as the middle value and most frequent value, respectively. Methods for calculating each from both types of data are shown. Other measures covered include the midrange and the effects of outliers. Shapes of distributions are discussed including positively and negatively skewed and symmetric. Practice problems are provided to reinforce the concepts.
4 3 Addition Rules for Probability
4 3 Addition Rules for Probabilitymlong24 This document discusses the addition rules for probability of compound events. It defines mutually exclusive events as events that cannot occur at the same time, and explains that for mutually exclusive events A and B, the probability of A or B is equal to the probability of A plus the probability of B. For events that are not mutually exclusive, the probability of A or B is equal to the probability of A plus the probability of B minus the probability of A and B occurring together. Several examples are provided to illustrate calculating probabilities using these addition rules.
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2.1 Phy I - Displacement and Velocitymlong24 This document discusses displacement, velocity, and related concepts. It begins by defining displacement as the straight-line change in an object's position, measured in meters. Displacement is distinguished from distance traveled, as displacement considers only the net change in position regardless of path. Equations for calculating average velocity from displacement and time are provided. Positive and negative displacement and velocity are described based on direction of motion. Instantaneous velocity is defined as the velocity at a specific point and can be determined from the slope of a tangent line on a position-time graph. Sample problems and practice questions are included for students to calculate displacement and average velocity in various scenarios.
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1.2 Measurements in Experiments
1.2 Measurements in Experimentsmlong24 This document provides an overview of measurements and significant figures in physics experiments. It defines key terms like accuracy, precision, and significant figures. Accuracy refers to how close a measurement is to the true value, while precision describes the exactness of a measurement. Significant figures indicate the precision of a measuring device. The document reviews proper techniques for measurements, unit conversions using dimensional analysis, and calculating with significant figures in additions, subtractions, multiplications and divisions. Students are expected to learn the basic SI units, prefixes, and how to determine the number of significant figures in given values and calculations.
1.1 What is Physics?
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1.3 The Language of Physics
1.3 The Language of Physicsmlong24 This document provides an introduction to the language of physics. It discusses how mathematical equations are used to describe physical phenomena and symbols commonly used in equations. Examples are provided on how to interpret equations and use dimensional analysis to check if equations are dimensionally consistent by evaluating the dimensions on each side. Students are assigned textbook problems evaluating physics expressions using these concepts.
AP Physics 1 - Introduction
AP Physics 1 - Introductionmlong24 This document provides an introduction to physics, covering several key topics:
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- Dimensional analysis is used to determine whether equations are valid by checking that quantities with the same dimensions can be combined and that both sides of an equation have the same dimensions.
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Color & Marketing
Color & Marketingmlong24 Branding uses colors, symbols, and designs to identify companies and convey instant messages to consumers. John Deere's iconic green and yellow symbol is instantly recognizable due to their strategic use of color in branding. McDonald's similarly uses the stimulating color red and friendly color yellow in their logo and marketing to trigger hunger and attract customers. Facebook's blue and white logo was influenced by Mark Zuckerberg's colorblindness but blue is also known to convey qualities of trust, loyalty, and communication that are suitable for a technology company. Color plays an important role in branding by increasing brand recognition and subliminally influencing consumer perceptions.
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- 2. To use the techniques of exploratory data analysis, including boxplots and five-number summaries, to discover various aspects of data.
- 3. Organizes data using frequency distribution Graphic Representation: Histogram, Frequency Polygon, Ogives Purpose: confirm various conjectures about the nature of the data.
- 4. Organizes data using stem and leaf plot Measure of central tendency used: median Measure of variation: IQR (Q3 – Q1) Graphic Representation: Box-and-Whisker Plot Purpose: To examine data to find out what information can be discovered about the data such as the center and the spread.
- 5. Five important values used: 1. MinimumValue 2. Q1 3. Median 4. Q3 5. MaximumValue
- 6. A boxplot is a graph of a data set obtained by 1. Drawing a horizontal line from the minimum value to Q1 2. Drawing a horizontal line from Q3 to the maximum value 3. Drawing a box whose vertical sides pass through Q1 and Q3 with a vertical line inside the box passing through Q2(the median)
- 7. A stockbroker recorded the number of clients she saw each day over an 11-day period. The data are shown. Construct a boxplot for the data. 33, 38, 43, 30, 29, 40, 51, 27, 42, 23, 31 1. Order: 23, 27, 29, 30, 31, 33, 38, 40, 42, 43, 51 2. Find median, Q1 and Q3: ▪ 33, 29, 42 3. Draw scale for data on x-axis 4. Locate the 5 numbers 5. Draw a box around Q1 and Q3, draw vertical line through the median, connect maximum and minimum values
- 8. If median is near center, distribution is appx symmetric. If median falls left of center, distribution is positively skewed. If median falls right of center, distribution is negatively skewed.
- 9. If lines are appx same length, distribution is appx symmetric. If right line is larger than the left, distribution is positively skewed. If left line is larger than the right, distribution is negatively skewed.
- 10. A resistant statistic is one that is not affected by an extremely skewed distribution. Examples: summary stats (median and IQR) A nonresistant statistic is one that is affected by an extremely skewed distribution. Examples: mean, standard deviation When distribution is skewed or contains outliers, median & IQR more accurately summarize the data.
- 11. p. 157-158 #1, 5, 7-10, 13, 15