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The Last Lecture by Randy Pausch l Summary & Study Guide

The Last Lecture by Randy Pausch l Summary & Study Guide

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Please wait.Published byTheodore O’Brien’ Modified over 2 years ago 1 DATA FROM A SAMPLE OF 25 STUDENTS ABBAB0 00BABB BB0A0 A000AB ABA0BA { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_1.jpg", "name": "DATA FROM A SAMPLE OF 25 STUDENTS ABBAB0 00BABB BB0A0 A000AB ABA0BA", "description": "DATA FROM A SAMPLE OF 25 STUDENTS ABBAB0 00BABB BB0A0 A000AB ABA0BA", "width": "800" } 2 112100127120134118105110109112 110118117116118122114 105109 107112114115118117118122106110 116108110121113120119111104111 120113120117105110118112114 DATA ON HIGH TEMPERATURES IN O F Source: The World Almanac and Book of Facts { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_2.jpg", "name": "112100127120134118105110109112 110118117116118122114 105109 107112114115118117118122106110 116108110121113120119111104111 120113120117105110118112114 DATA ON HIGH TEMPERATURES IN O F Source: The World Almanac and Book of Facts", "description": "112100127120134118105110109112 110118117116118122114 105109 107112114115118117118122106110 116108110121113120119111104111 120113120117105110118112114 DATA ON HIGH TEMPERATURES IN O F Source: The World Almanac and Book of Facts", "width": "800" } 3 DATA ON TESTING CENTER THE NUMBER OF CARDIOGRAM PERFORMED EACH DAY FOR 20 DAYS 2531203213 144325723 3632333244 3252445145 { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_3.jpg", "name": "DATA ON TESTING CENTER THE NUMBER OF CARDIOGRAM PERFORMED EACH DAY FOR 20 DAYS 2531203213 144325723 3632333244 3252445145", "description": "DATA ON TESTING CENTER THE NUMBER OF CARDIOGRAM PERFORMED EACH DAY FOR 20 DAYS 2531203213 144325723 3632333244 3252445145", "width": "800" } 4 Dot Plot is a graphical summaries of data. A horizontal axis shows the range of values for the observations. Each data value is represented by a dot placed above the axis. Dot Plots show the details of the data and are useful for comparing the distribution of data for 2 or more variables. DOT DIAGRAM (DOT PLOT) { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_4.jpg", "name": "Dot Plot is a graphical summaries of data.", "description": "A horizontal axis shows the range of values for the observations. Each data value is represented by a dot placed above the axis. Dot Plots show the details of the data and are useful for comparing the distribution of data for 2 or more variables. DOT DIAGRAM (DOT PLOT).", "width": "800" } 5 The data shown below are the number of case files for 30 “law and consulting firms” in Ankara in 2010. 83 83 75 80 76 80 81 84 79 80 84 86 72 82 82 79 81 79 80 73 90 82 81 75 77 80 79 76 85 85 Construct a dot plot of the given above data. { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_5.jpg", "name": "The data shown below are the number of case files for 30 law and consulting firms in Ankara in 2010.", "description": "83 83 75 80 76 80 81 84 79 80 84 86 72 82 82 79 81 79 80 73 90 82 81 75 77 80 79 76 85 85 Construct a dot plot of the given above data..", "width": "800" } 6 THE STEM_AND_LEAF DISPLAY This technique can be used to show both the rank order and a shape of a data set simultaneously. To develop a stem_and_leaf display, we first arrange the leading digits of each data value to the left of the vertical line. To the right of the vertical line, we record the last digit for each data value as we pass through the observations in the order they were recorded. The numbers to the left of the vertical line form the STEM. Each digit to the right of the vertical line is a LEAF Advantages: 1- It is easier to construct by hand 2- Since it shows the actual data, this display provides more information than histograms { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_6.jpg", "name": "THE STEM_AND_LEAF DISPLAY This technique can be used to show both the rank order and a shape of a data set simultaneously.", "description": "To develop a stem_and_leaf display, we first arrange the leading digits of each data value to the left of the vertical line. To the right of the vertical line, we record the last digit for each data value as we pass through the observations in the order they were recorded. The numbers to the left of the vertical line form the STEM. Each digit to the right of the vertical line is a LEAF Advantages: 1- It is easier to construct by hand 2- Since it shows the actual data, this display provides more information than histograms.", "width": "800" } 7 { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_7.jpg", "name": "", "description": "", "width": "800" } 8 FREQUENCY DISTRIBUTION FOR QUALITATIVE DATA { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_8.jpg", "name": "FREQUENCY DISTRIBUTION FOR QUALITATIVE DATA", "description": "FREQUENCY DISTRIBUTION FOR QUALITATIVE DATA", "width": "800" } 9 Criminal Divorce Commercial Divorce Criminal Cyber Divorce Commercial Criminal Cyber Insurance Criminal Divorce Criminal Insurance Criminal Divorce Criminal Divorce Criminal Insurance Commercial Criminal Commercial Criminal Insurance Cyber Commercial Divorce Commercial Criminal Commercial Cyber Criminal Divorce Commercial Criminal Cyber Commercial Insurance DATA FROM A SAMPLE OF 50 CASES IN A LAW FIRM { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_9.jpg", "name": "Criminal Divorce Commercial Divorce Criminal Cyber Divorce Commercial Criminal Cyber Insurance Criminal Divorce Criminal Insurance Criminal Divorce Criminal Divorce Criminal Insurance Commercial Criminal Commercial Criminal Insurance Cyber Commercial Divorce Commercial Criminal Commercial Cyber Criminal Divorce Commercial Criminal Cyber Commercial Insurance DATA FROM A SAMPLE OF 50 CASES IN A LAW FIRM", "description": "Criminal Divorce Commercial Divorce Criminal Cyber Divorce Commercial Criminal Cyber Insurance Criminal Divorce Criminal Insurance Criminal Divorce Criminal Divorce Criminal Insurance Commercial Criminal Commercial Criminal Insurance Cyber Commercial Divorce Commercial Criminal Commercial Cyber Criminal Divorce Commercial Criminal Cyber Commercial Insurance DATA FROM A SAMPLE OF 50 CASES IN A LAW FIRM", "width": "800" } 10 BAR GRAPHS A Bar graph (chart) is a graphical device for depicting qualitative data summarized in: -Frequency -Relative Frequency -Percent Frequency On one axis of the graph (usually the horizontal axis) we specify the labels that are used for the classes (Categories) of data. A frequency; relative frequency or percent frequency scale can be used for the other axis of the graph (usually the vertical axis) { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_10.jpg", "name": "BAR GRAPHS A Bar graph (chart) is a graphical device for depicting qualitative data summarized in: -Frequency -Relative Frequency -Percent Frequency On one axis of the graph (usually the horizontal axis) we specify the labels that are used for the classes (Categories) of data.", "description": "A frequency; relative frequency or percent frequency scale can be used for the other axis of the graph (usually the vertical axis).", "width": "800" } 11 FREQUENCY DISTRIBUTION FOR QUANTITATIVE DATA With quantitative data, we must be more careful in defining the non-overlapping classes to be used in the freq. distribution. There are three steps necessary to define classes for a freq. diApp. with quantitative data. 1- Determine the number of non-overlapping classes. 2- Determine the width of each class. 3- Determine the class limits 1- NUMBER OF CLASSES: Classes are formed by specifying ranges that will be used to group the data. As a general guideline, we recommend using between 5 and 20 classes. For a small number of data items, as few as five or six classes may be used to summarize the data. Sample SizeNumber of Classes Fewer than 505 – 6 Classes 50 to 1007 – 8 Classes Over 1009 – 10 Classes { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_11.jpg", "name": "FREQUENCY DISTRIBUTION FOR QUANTITATIVE DATA With quantitative data, we must be more careful in defining the non-overlapping classes to be used in the freq.", "description": "distribution. There are three steps necessary to define classes for a freq. diApp. with quantitative data. 1- Determine the number of non-overlapping classes. 2- Determine the width of each class. 3- Determine the class limits 1- NUMBER OF CLASSES: Classes are formed by specifying ranges that will be used to group the data. As a general guideline, we recommend using between 5 and 20 classes. For a small number of data items, as few as five or six classes may be used to summarize the data. Sample SizeNumber of Classes Fewer than 505 – 6 Classes 50 to 1007 – 8 Classes Over 1009 – 10 Classes.", "width": "800" } 12 3- CLASS LIMITS: Class limits must be chosen so that each item belongs to one and only one class. The lower class limit identifies the smallest possible data value assigned to the class. The upper class limit identifies the largest possible data value assigned to the class. { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_12.jpg", "name": "3- CLASS LIMITS: Class limits must be chosen so that each item belongs to one and only one class.", "description": "The lower class limit identifies the smallest possible data value assigned to the class. The upper class limit identifies the largest possible data value assigned to the class..", "width": "800" } 13 CLASS MIDPOINT (M): The class midpoint is the value halfway between the lower and upper class limits. The definitions of the Relative Freq. and Percent Freq. Distributions are as the same as for qualitative data Cumulative Distributions: Cumulative Distribution is another tabular summary of data (quantitative). Cumulative Distribution use the number of classes, class widths and class limits developed for the frequency distributions. Cumulative Distribution shows the number of data items with values “less than or equal to the upper class limit” of each class. We also note that a cumulative relative frequency distribution shows the proportion of data items, and a cumulative percent frequency distribution shows the percentage of data items with values less than or equal to the upper limit of each class. { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_13.jpg", "name": "CLASS MIDPOINT (M): The class midpoint is the value halfway between the lower and upper class limits.", "description": "The definitions of the Relative Freq. and Percent Freq. Distributions are as the same as for qualitative data Cumulative Distributions: Cumulative Distribution is another tabular summary of data (quantitative). Cumulative Distribution use the number of classes, class widths and class limits developed for the frequency distributions. Cumulative Distribution shows the number of data items with values less than or equal to the upper class limit of each class. We also note that a cumulative relative frequency distribution shows the proportion of data items, and a cumulative percent frequency distribution shows the percentage of data items with values less than or equal to the upper limit of each class..", "width": "800" } 14 Administrators of a company are considering the possibility of changing the pattern of work hours from 8-hours-day, 5-day week to a 10-hours day, 4-day week. They feel that this change might cut down on absenteeism. In order to help make this decision, the following data were collected on the number of workers absent per day over a 6-week experimental period. Construct a frequency distribution table for these data 159 516 307129 2315211617132018231 1112272215141061914 { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_14.jpg", "name": "Administrators of a company are considering the possibility of changing the pattern of work hours from 8-hours-day, 5-day week to a 10-hours day, 4-day week.", "description": "They feel that this change might cut down on absenteeism. In order to help make this decision, the following data were collected on the number of workers absent per day over a 6-week experimental period. Construct a frequency distribution table for these data 159 516 307129 2315211617132018231 1112272215141061914.", "width": "800" } 15 { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_15.jpg", "name": "", "description": "", "width": "800" } 16 HISTOGRAM A common graphical presentation for quantitative data is a Histogram. This graphical summary can be prepared for data previously summarized in either a frequency, relative frequency or percent frequency distributions. Variable of interest is placed on the horizontal axis. The frequency/relative/percent frequency is placed on the vertical axis for each class which is shown by drawing a rectangle whose base is determined by the class limits on the horizontal axis and whose height is the corresponding frequency/relative/percent frequency. { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_16.jpg", "name": "HISTOGRAM A common graphical presentation for quantitative data is a Histogram.", "description": "This graphical summary can be prepared for data previously summarized in either a frequency, relative frequency or percent frequency distributions. Variable of interest is placed on the horizontal axis. The frequency/relative/percent frequency is placed on the vertical axis for each class which is shown by drawing a rectangle whose base is determined by the class limits on the horizontal axis and whose height is the corresponding frequency/relative/percent frequency..", "width": "800" } 17 PIE CHART The pie chart provides another graphical device for presenting frequency and percent frequency distribution for qualitative data. To construct a pie chart: We first draw a circle to represent all of the data. Then we use the relative frequencies to subdivide the circle into sectors, or parts that correspond to the relative frequency for each class. { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/23/6835136/slides/slide_17.jpg", "name": "PIE CHART The pie chart provides another graphical device for presenting frequency and percent frequency distribution for qualitative data.", "description": "To construct a pie chart: We first draw a circle to represent all of the data. Then we use the relative frequencies to subdivide the circle into sectors, or parts that correspond to the relative frequency for each class..", "width": "800" } Ppt on regional transport office delhi By appt only movie main Ppt on producers consumers and decomposers in the forest Training ppt on motivation Ppt on machine translation service Ppt on 555 timer oscillator Ppt on human chromosomes genes Ppt on viruses and bacteria differences Habitats for kids ppt on batteries Ppt on bond length chemistry ISTANBUL STOCK EXCHANGE (BIST) FELL 6 POINTS IN AVERAGE TODAY THE UNITED STATES DOLLAR (USD) APPRECIATED BY 4 PERCENT LAST WEEK AGAINST TURKISH LIRA (TRL).Dot Plot is a graphical summaries of data. A horizontal axis shows the range of values for the observations. Each data value is represented by a dot placed.1 1 Slide STATISTICS FOR BUSINESS AND ECONOMICS Seventh Edition AndersonSweeneyWilliams Slides Prepared by John Loucks © 1999 ITP/South-Western College.QMS 6351 Statistics and Research Methods Chapter 2 Descriptive Statistics: Tabular and Graphical Methods Prof. Vera Adamchik.1 Probabilistic and Statistical Techniques Lecture 3 Dr. Nader Okasha.Chapter 2, Part A Descriptive Statistics: Tabular and Graphical Presentations n Summarizing Categorical Data n Summarizing Quantitative Data Categorical.1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.Census A survey to collect data on the entire population.   Data The facts and figures collected, yzed, and summarized for presentation and.1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.1 1 Slide IS 310 – Business Statistics IS 310 Business Statistics CSU Long Beach.1 1 Slide IS 310 – Business Statistics IS 310 Business Statistics CSU Long Beach.1 1 Slide © 2005 Thomson/South-Western Introduction to Statistics Chapter 2 Descriptive Statistics.1 1 Slide © 2009 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.2.1 Summarizing Qualitative Data  A graphic display can reveal at a glance the main characteristics of a data set.  Three types of graphs used to display.© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.When data is collected from a survey or designed experiment, they must be organized into a manageable form. Data that is not organized is referred to as.© 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd.. 1 Slide Slide Slides Prepared by Juei-Chao Chen Fu Jen Catholic University Slides Prepared.1 1 Slide © University of Minnesota-Duluth, Summer 2009-Econ-2030(Dr. Tadesse) Chapter 2 Descriptive Statistics.Chapter 2 Summarizing and Graphing Data  Frequency Distributions  Histograms  Statistical Graphics such as stemplots, dotplots, boxplots, etc.  Boxplots. Frequency Distribution is a statistical technique to explore the underlying patterns of raw data.  Preparing frequency distribution tables, we can. Similar presentations © 2017 SlidePlayer.com Inc. All rights reserved.







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