This document discusses key concepts in statistics including descriptive and inferential statistics, populations and samples, variables, and methods of collecting and presenting data. Specifically, it defines statistics, the two main types (descriptive and inferential), populations as all elements studied and samples as subsets of populations. It also outlines common variable types, methods of collecting data, different sampling techniques, how to construct frequency distributions and cumulative frequency distributions for qualitative and quantitative variables, and how to present data using bar charts and histograms.
This document discusses frequency distributions and graphs. It defines frequency distributions as organizing raw data into a table using classes and frequencies. There are three main types: categorical, grouped, and ungrouped. Guidelines are provided for constructing frequency distributions, such as having 5-20 classes of equal width. Common graphs discussed are histograms, frequency polygons, ogives, Pareto charts, time series graphs, and pie charts. These graphs represent frequency distributions in visual formats.
This document discusses frequency distributions and graphs. It defines frequency distributions as organizing raw data into a table using classes and frequencies. There are three main types: categorical, grouped, and ungrouped. Guidelines are provided for constructing frequency distributions, such as having 5-20 classes of equal width. Common graphs discussed are histograms, frequency polygons, ogives, Pareto charts, time series graphs, and pie charts. These graphs represent frequency distributions in visual formats.
Descriptive statistics can summarize and graphically present data. Tabular presentations display data in a grid, with tables showing frequencies of categories. Graphical presentations include bar graphs to show frequencies, pie charts to show proportions, and line graphs to show trends over time. Frequency distributions organize raw data into meaningful patterns for analysis by specifying class intervals and calculating frequencies and cumulative frequencies.
Taking of a measurement and the process of counting yield numbers that contain information. The objective of a person applying the tools of statistics to these numbers is to determine the nature of this information.
This task is made much easier if the numbers are organized and summarized.
Even quite small data sets are difficult to understand without some summarization. Statistical quantities such as the mean and variance can be extremely helpful in summarizing data but first we discuss tabular and graphical summaries.
There are several ways to present a statistical data like;
Frequency table
Simple bar diagrams
Multiple Bar Diagrams
Histogram
Frequency Polygon etc.
Steam and Leaf plots
Pie Charts
A frequency distribution is a tabular arrangement of data in which various items are arranged into classes or groups and the number of items falling in each class is stated.
The number of observations falling in a particular class is referred to as class frequency and is denoted by "f".
In frequency distribution all the values falling in a class are assumed to be equal to the midpoint of that class.
Data presented in the form of a frequency distribution is also called grouped data. A frequency distribution table contains a condensed summary of the original data.
There are two types of frequency distribution i) Simple Frequency distribution ) ii) Grouped Frequency distribution.
Principlles of statistics [amar mamusta amir]Rebin Daho
This document discusses key concepts in statistics including descriptive and inferential statistics, populations and samples, variables, levels of measurement, and frequency distributions. It defines statistics as obtaining, organizing, analyzing, and drawing conclusions from data. Descriptive statistics summarize and descriptive data while inferential statistics make conclusions beyond the data. It also outlines different types of variables, levels of measurement for variables, and methods for constructing frequency distributions for qualitative and quantitative data. Frequency distributions organize raw data into classes and frequencies to facilitate analysis and interpretation.
In this lesson we enrich what the students have already learned from Grade 1 to 10 about presenting data. Additional concepts could help the students to appropriately describe further the data set.
This document discusses key concepts in statistics including descriptive and inferential statistics, populations and samples, variables, and methods of collecting and presenting data. Specifically, it defines statistics, the two main types (descriptive and inferential), populations as all elements studied and samples as subsets of populations. It also outlines common variable types, methods of collecting data, different sampling techniques, how to construct frequency distributions and cumulative frequency distributions for qualitative and quantitative variables, and how to present data using bar charts and histograms.
This document discusses frequency distributions and graphs. It defines frequency distributions as organizing raw data into a table using classes and frequencies. There are three main types: categorical, grouped, and ungrouped. Guidelines are provided for constructing frequency distributions, such as having 5-20 classes of equal width. Common graphs discussed are histograms, frequency polygons, ogives, Pareto charts, time series graphs, and pie charts. These graphs represent frequency distributions in visual formats.
This document discusses frequency distributions and graphs. It defines frequency distributions as organizing raw data into a table using classes and frequencies. There are three main types: categorical, grouped, and ungrouped. Guidelines are provided for constructing frequency distributions, such as having 5-20 classes of equal width. Common graphs discussed are histograms, frequency polygons, ogives, Pareto charts, time series graphs, and pie charts. These graphs represent frequency distributions in visual formats.
Descriptive statistics can summarize and graphically present data. Tabular presentations display data in a grid, with tables showing frequencies of categories. Graphical presentations include bar graphs to show frequencies, pie charts to show proportions, and line graphs to show trends over time. Frequency distributions organize raw data into meaningful patterns for analysis by specifying class intervals and calculating frequencies and cumulative frequencies.
Taking of a measurement and the process of counting yield numbers that contain information. The objective of a person applying the tools of statistics to these numbers is to determine the nature of this information.
This task is made much easier if the numbers are organized and summarized.
Even quite small data sets are difficult to understand without some summarization. Statistical quantities such as the mean and variance can be extremely helpful in summarizing data but first we discuss tabular and graphical summaries.
There are several ways to present a statistical data like;
Frequency table
Simple bar diagrams
Multiple Bar Diagrams
Histogram
Frequency Polygon etc.
Steam and Leaf plots
Pie Charts
A frequency distribution is a tabular arrangement of data in which various items are arranged into classes or groups and the number of items falling in each class is stated.
The number of observations falling in a particular class is referred to as class frequency and is denoted by "f".
In frequency distribution all the values falling in a class are assumed to be equal to the midpoint of that class.
Data presented in the form of a frequency distribution is also called grouped data. A frequency distribution table contains a condensed summary of the original data.
There are two types of frequency distribution i) Simple Frequency distribution ) ii) Grouped Frequency distribution.
Principlles of statistics [amar mamusta amir]Rebin Daho
This document discusses key concepts in statistics including descriptive and inferential statistics, populations and samples, variables, levels of measurement, and frequency distributions. It defines statistics as obtaining, organizing, analyzing, and drawing conclusions from data. Descriptive statistics summarize and descriptive data while inferential statistics make conclusions beyond the data. It also outlines different types of variables, levels of measurement for variables, and methods for constructing frequency distributions for qualitative and quantitative data. Frequency distributions organize raw data into classes and frequencies to facilitate analysis and interpretation.
In this lesson we enrich what the students have already learned from Grade 1 to 10 about presenting data. Additional concepts could help the students to appropriately describe further the data set.
A frequency distribution arranges data into classes and shows the number of observations in each class. It displays grouped data with the class boundaries, midpoints, frequencies, and cumulative frequencies. To construct a frequency distribution, the number of classes is determined, the class interval size is calculated, and the data is distributed into the appropriate classes. The frequency distribution provides an organized summary of the data in a table.
This document discusses frequency distributions and methods for graphically presenting frequency distribution data. It defines a frequency distribution as a tabulation or grouping of data into categories showing the number of observations in each group. The document outlines the parts of a frequency table as class limits, class size, class boundaries, and class marks. It then provides steps for constructing a frequency distribution table from a set of data. Finally, it discusses histograms and frequency polygons as methods for graphically presenting frequency distribution data, and provides examples of how to construct these graphs in Excel.
This document discusses various methods for presenting data, including tabular form, arrays, simple tables, frequency distributions, and stem-and-leaf displays. It provides examples and tasks to practice each method. Specifically, it discusses how to construct frequency distributions and stem-and-leaf displays, including how to determine class limits, boundaries, widths, and marks. The goal is to organize and present data in a meaningful way that allows for easy interpretation and analysis.
The document discusses different methods for presenting data, including textual, tabular, and graphical presentations. It provides examples and guidelines for each method, such as describing highlights in a paragraph, organizing values into a table with rows and columns, and using graphs like pie charts to visualize relationships. Frequency distribution tables and histograms are also covered as specialized forms of tabular and graphical presentation used to depict the distribution of quantitative data.
This document discusses different types of graphs and distributions that can be used to organize and represent data. It explains frequency distributions, histograms, frequency polygons, ogives, relative frequency graphs, Pareto charts, time series graphs, pie charts, and stem-and-leaf plots. Rules for constructing frequency distributions and examples of each type of graph are provided.
This document discusses different types of graphs and distributions that can be used to organize and represent data. It covers frequency distributions, histograms, frequency polygons, ogives, relative frequency graphs, Pareto charts, time series graphs, pie charts, and stem-and-leaf plots. Rules for constructing frequency distributions are provided, such as having between 5-20 classes and equal class widths. Examples are given to illustrate each type of graph or distribution.
The document discusses descriptive statistics and provides definitions of key terms. It introduces population and sample, qualitative and quantitative variables, methods of presenting data through tables and graphs, and measures of central tendency including mean, median, and mode. It also outlines the stages of statistical analysis and provides examples of calculating central measures for both raw and grouped data.
The document outlines key concepts in statistics including frequency distributions, measures of central tendency, dispersion, position, and distribution. It discusses grouped and ungrouped data, mean, median, mode, range, variance, standard deviation, quartiles, percentiles, skewness, kurtosis, and z-scores. Graphs like histograms, pie charts, and ogives are presented as ways to visually represent data. Formulas and examples are provided for calculating various statistical measures.
1. The document describes how to construct and analyze frequency distributions, which organize data into classes and count the number of data points in each class.
2. It provides guidelines for constructing a frequency distribution, such as choosing the number of classes and calculating class widths and limits.
3. Various graphs can be created from a frequency distribution, including histograms, polygons, and cumulative frequency graphs, to visualize patterns in the data distribution.
The document describes various methods for constructing and interpreting frequency distributions and graphs, including:
1) Constructing a frequency distribution involves deciding on class intervals, calculating class widths and limits, tallying data points, and counting frequencies.
2) Additional metrics like midpoints, relative frequencies, and cumulative frequencies can provide more information about the distribution.
3) Graphs like histograms, frequency polygons, relative frequency histograms, and cumulative frequency graphs visually represent the distribution using bars or lines.
This document provides an overview of descriptive statistics and statistical concepts. It discusses topics such as data collection, organization, analysis, interpretation and presentation. It also covers frequency distributions, measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and hypothesis testing. Hypothesis testing involves forming a null hypothesis and alternative hypothesis, and using statistical tests to either reject or fail to reject the null hypothesis based on sample data. Common statistical tests include ones for comparing means, variances or proportions.
This document discusses organizing and presenting data through descriptive statistics. It describes various types of descriptive statistics including measures to condense data like frequency distributions and graphic presentations. It then provides examples and steps for creating frequency distribution tables and different types of graphs like bar charts, histograms, line graphs, scatterplots and pie charts to summarize both qualitative and quantitative data.
TSTD 6251 Fall 2014SPSS Exercise and Assignment 120 PointsI.docxnanamonkton
TSTD 6251 Fall 2014
SPSS Exercise and Assignment 1
20 Points
In this class, we are going to study descriptive summary statistics and learn how to construct box plot. We are still working with univariate variable for this exercise.
Practice Example:
Admission receipts (in million of dollars) for a recent season are given below for the
n =
30 major league baseball teams:
19.4 26.6 22.9 44.5 24.4 19.0 27.5 19.9 22.8 19.0 16.9 15.2 25.7 19.0 15.5 17.1 15.6 10.6 16.2 15.6 15.4 18.2 15.5 14.2 9.5 9.9
10.7 11.9 26.7 17.5
Require:
a. Compute the mean, variance and standard deviation.
b. Find the sample median, first quartile, and third quartile.
c. Construct a boxplot and interpret the distribution of the data.
d. Discuss the distribution of this set of data by examining kurtosis and skewness
statistics, such as if the distribution is skewed to one side of the distribution, and if the
distribution shows a peaked/skinny curve or a spread out/flat curve.
SPSS Procedures for Computing Summary Statistics
:
Enter the 30 data values in the first column of SPSS
Data View
Tab
Variable View
and name this variable
receipts
Adjust
Decimals
to 3 decimal points
Type
Admission Receipts
($ mn)
in the
Label
column for output viewer
Return to
Data View
and click
A
nalyze
on the menu bar
Click the second menu
D
e
scriptive Statistics
Click
F
requencies …
Move
Admission Receipts
to the
Variable(s)
list by clicking the arrow button
Click
S
tatistics …
button at the top of the dialog box
Now, you can select the descriptive statistics according to what the question requires. For this practice question, it requires central tendency, dispersion, percentile and distribution statistics, so we click all the boxes
except for
P
ercentile(s): and Va
l
ues are group midpoints
.
Click
Continue
to return to the
Frequencies
dialog box
Click
OK
to generate descriptive statistic output which is pasted below:
The first table provides summary statistics and the second table lists frequencies, relative frequencies and cumulative frequencies. The statistics required for solving this problem are highlighted in red.
Statistics
Admission Receipts
N
Valid
30
Missing
0
Mean
18.76333
Std. Error of Mean
1.278590
Median
17.30000
Mode
19.000
Std. Deviation
7.003127
Variance
49.043782
Skewness
1.734
Std. Error of Skewness
.427
Kurtosis
5.160
Std. Error of Kurtosis
.833
Range
35.000
Minimum
9.500
Maximum
44.500
Sum
562.900
Percentiles
10
10.61000
20
14.40000
25
15.35000
30
15.50000
40
15.84000
50
17.30000
60
19.00000
70
19.75000
75
22.82500
80
24.10000
90
26.69000
Admission Receipts
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
9.500
1
3.3
3.3
3.3
9.900
1
3.3
3.3
6.7
10.600
1
3.3
3.3
10.0
10.700
1
3.3
3.3
13.3
11.900
1
3.3
3.3
16.7
14.200
1
3.3
3.3
20.0
15.2.
This document provides an overview of quantitative descriptive research and statistics. It defines levels of measurement as nominal, ordinal, interval, and ratio scales. Descriptive statistics are used to summarize data through measures of central tendency like mean, median, and mode as well as measures of variability like standard deviation. Nominal data is described through frequencies and percentages. Ordinal and interval data can also be described graphically through stem-and-leaf plots and evaluations of distributions, skewness, and kurtosis. Reliability of measures is determined through methods like split-half analysis and Cronbach's alpha.
This document discusses various methods of presenting statistical data, including tabulation, graphs, and diagrams. It describes frequency distribution tables, histograms, frequency polygons, frequency curves, cumulative frequency diagrams, line charts, scatter diagrams, bar diagrams, pie charts, pictograms, and map diagrams. The key methods are:
1. Tabulation involves organizing data into frequency distribution tables to group observations.
2. Graphs such as histograms, frequency polygons, and frequency curves can be used to present quantitative continuous data visually.
3. Diagrams including bar diagrams, pie charts, and pictograms present qualitative discrete data. Map diagrams show geographic distributions.
This document provides an overview of chapter 2 from an elementary statistics textbook. It covers exploring and organizing data using frequency distributions, histograms, graphs, scatterplots, and other methods. The objectives are to organize data using frequency distributions and represent data graphically. It defines key terms like population, sample, parameter, and statistic. It also describes procedures for constructing frequency distributions and calculating cumulative frequencies. Examples are provided to demonstrate how to organize various data sets into frequency distributions.
This section expands on frequency distributions by discussing additional features: midpoints, which are the averages of class limits; relative frequency, which shows what portion of the data falls in each class; and cumulative frequency, which is the running total of all previous classes' frequencies. It provides an example calculating these values for a given
This document provides an overview of descriptive statistics concepts and methods. It discusses numerical summaries of data like measures of central tendency (mean, median, mode) and variability (standard deviation, variance, range). It explains how to calculate and interpret these measures. Examples are provided to demonstrate calculating measures for sample data and interpreting what they say about the data distribution. Frequency distributions and histograms are also introduced as ways to visually summarize and understand the characteristics of data.
The document discusses tabulation and its importance. It provides objectives and rules for tabulating data, including arranging it logically and including totals. It describes key parts of an ideal table like the title, columns, body, and sources. Different types of tabulation are covered, including simple, grouped, and cross tabulation. Grouped frequency tables involve dividing a range into class intervals. Cumulative frequency tables show the sum of frequencies up to a level. Cross tabulation allows comparison of how respondents answered two questions.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
A frequency distribution arranges data into classes and shows the number of observations in each class. It displays grouped data with the class boundaries, midpoints, frequencies, and cumulative frequencies. To construct a frequency distribution, the number of classes is determined, the class interval size is calculated, and the data is distributed into the appropriate classes. The frequency distribution provides an organized summary of the data in a table.
This document discusses frequency distributions and methods for graphically presenting frequency distribution data. It defines a frequency distribution as a tabulation or grouping of data into categories showing the number of observations in each group. The document outlines the parts of a frequency table as class limits, class size, class boundaries, and class marks. It then provides steps for constructing a frequency distribution table from a set of data. Finally, it discusses histograms and frequency polygons as methods for graphically presenting frequency distribution data, and provides examples of how to construct these graphs in Excel.
This document discusses various methods for presenting data, including tabular form, arrays, simple tables, frequency distributions, and stem-and-leaf displays. It provides examples and tasks to practice each method. Specifically, it discusses how to construct frequency distributions and stem-and-leaf displays, including how to determine class limits, boundaries, widths, and marks. The goal is to organize and present data in a meaningful way that allows for easy interpretation and analysis.
The document discusses different methods for presenting data, including textual, tabular, and graphical presentations. It provides examples and guidelines for each method, such as describing highlights in a paragraph, organizing values into a table with rows and columns, and using graphs like pie charts to visualize relationships. Frequency distribution tables and histograms are also covered as specialized forms of tabular and graphical presentation used to depict the distribution of quantitative data.
This document discusses different types of graphs and distributions that can be used to organize and represent data. It explains frequency distributions, histograms, frequency polygons, ogives, relative frequency graphs, Pareto charts, time series graphs, pie charts, and stem-and-leaf plots. Rules for constructing frequency distributions and examples of each type of graph are provided.
This document discusses different types of graphs and distributions that can be used to organize and represent data. It covers frequency distributions, histograms, frequency polygons, ogives, relative frequency graphs, Pareto charts, time series graphs, pie charts, and stem-and-leaf plots. Rules for constructing frequency distributions are provided, such as having between 5-20 classes and equal class widths. Examples are given to illustrate each type of graph or distribution.
The document discusses descriptive statistics and provides definitions of key terms. It introduces population and sample, qualitative and quantitative variables, methods of presenting data through tables and graphs, and measures of central tendency including mean, median, and mode. It also outlines the stages of statistical analysis and provides examples of calculating central measures for both raw and grouped data.
The document outlines key concepts in statistics including frequency distributions, measures of central tendency, dispersion, position, and distribution. It discusses grouped and ungrouped data, mean, median, mode, range, variance, standard deviation, quartiles, percentiles, skewness, kurtosis, and z-scores. Graphs like histograms, pie charts, and ogives are presented as ways to visually represent data. Formulas and examples are provided for calculating various statistical measures.
1. The document describes how to construct and analyze frequency distributions, which organize data into classes and count the number of data points in each class.
2. It provides guidelines for constructing a frequency distribution, such as choosing the number of classes and calculating class widths and limits.
3. Various graphs can be created from a frequency distribution, including histograms, polygons, and cumulative frequency graphs, to visualize patterns in the data distribution.
The document describes various methods for constructing and interpreting frequency distributions and graphs, including:
1) Constructing a frequency distribution involves deciding on class intervals, calculating class widths and limits, tallying data points, and counting frequencies.
2) Additional metrics like midpoints, relative frequencies, and cumulative frequencies can provide more information about the distribution.
3) Graphs like histograms, frequency polygons, relative frequency histograms, and cumulative frequency graphs visually represent the distribution using bars or lines.
This document provides an overview of descriptive statistics and statistical concepts. It discusses topics such as data collection, organization, analysis, interpretation and presentation. It also covers frequency distributions, measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and hypothesis testing. Hypothesis testing involves forming a null hypothesis and alternative hypothesis, and using statistical tests to either reject or fail to reject the null hypothesis based on sample data. Common statistical tests include ones for comparing means, variances or proportions.
This document discusses organizing and presenting data through descriptive statistics. It describes various types of descriptive statistics including measures to condense data like frequency distributions and graphic presentations. It then provides examples and steps for creating frequency distribution tables and different types of graphs like bar charts, histograms, line graphs, scatterplots and pie charts to summarize both qualitative and quantitative data.
TSTD 6251 Fall 2014SPSS Exercise and Assignment 120 PointsI.docxnanamonkton
TSTD 6251 Fall 2014
SPSS Exercise and Assignment 1
20 Points
In this class, we are going to study descriptive summary statistics and learn how to construct box plot. We are still working with univariate variable for this exercise.
Practice Example:
Admission receipts (in million of dollars) for a recent season are given below for the
n =
30 major league baseball teams:
19.4 26.6 22.9 44.5 24.4 19.0 27.5 19.9 22.8 19.0 16.9 15.2 25.7 19.0 15.5 17.1 15.6 10.6 16.2 15.6 15.4 18.2 15.5 14.2 9.5 9.9
10.7 11.9 26.7 17.5
Require:
a. Compute the mean, variance and standard deviation.
b. Find the sample median, first quartile, and third quartile.
c. Construct a boxplot and interpret the distribution of the data.
d. Discuss the distribution of this set of data by examining kurtosis and skewness
statistics, such as if the distribution is skewed to one side of the distribution, and if the
distribution shows a peaked/skinny curve or a spread out/flat curve.
SPSS Procedures for Computing Summary Statistics
:
Enter the 30 data values in the first column of SPSS
Data View
Tab
Variable View
and name this variable
receipts
Adjust
Decimals
to 3 decimal points
Type
Admission Receipts
($ mn)
in the
Label
column for output viewer
Return to
Data View
and click
A
nalyze
on the menu bar
Click the second menu
D
e
scriptive Statistics
Click
F
requencies …
Move
Admission Receipts
to the
Variable(s)
list by clicking the arrow button
Click
S
tatistics …
button at the top of the dialog box
Now, you can select the descriptive statistics according to what the question requires. For this practice question, it requires central tendency, dispersion, percentile and distribution statistics, so we click all the boxes
except for
P
ercentile(s): and Va
l
ues are group midpoints
.
Click
Continue
to return to the
Frequencies
dialog box
Click
OK
to generate descriptive statistic output which is pasted below:
The first table provides summary statistics and the second table lists frequencies, relative frequencies and cumulative frequencies. The statistics required for solving this problem are highlighted in red.
Statistics
Admission Receipts
N
Valid
30
Missing
0
Mean
18.76333
Std. Error of Mean
1.278590
Median
17.30000
Mode
19.000
Std. Deviation
7.003127
Variance
49.043782
Skewness
1.734
Std. Error of Skewness
.427
Kurtosis
5.160
Std. Error of Kurtosis
.833
Range
35.000
Minimum
9.500
Maximum
44.500
Sum
562.900
Percentiles
10
10.61000
20
14.40000
25
15.35000
30
15.50000
40
15.84000
50
17.30000
60
19.00000
70
19.75000
75
22.82500
80
24.10000
90
26.69000
Admission Receipts
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
9.500
1
3.3
3.3
3.3
9.900
1
3.3
3.3
6.7
10.600
1
3.3
3.3
10.0
10.700
1
3.3
3.3
13.3
11.900
1
3.3
3.3
16.7
14.200
1
3.3
3.3
20.0
15.2.
This document provides an overview of quantitative descriptive research and statistics. It defines levels of measurement as nominal, ordinal, interval, and ratio scales. Descriptive statistics are used to summarize data through measures of central tendency like mean, median, and mode as well as measures of variability like standard deviation. Nominal data is described through frequencies and percentages. Ordinal and interval data can also be described graphically through stem-and-leaf plots and evaluations of distributions, skewness, and kurtosis. Reliability of measures is determined through methods like split-half analysis and Cronbach's alpha.
This document discusses various methods of presenting statistical data, including tabulation, graphs, and diagrams. It describes frequency distribution tables, histograms, frequency polygons, frequency curves, cumulative frequency diagrams, line charts, scatter diagrams, bar diagrams, pie charts, pictograms, and map diagrams. The key methods are:
1. Tabulation involves organizing data into frequency distribution tables to group observations.
2. Graphs such as histograms, frequency polygons, and frequency curves can be used to present quantitative continuous data visually.
3. Diagrams including bar diagrams, pie charts, and pictograms present qualitative discrete data. Map diagrams show geographic distributions.
This document provides an overview of chapter 2 from an elementary statistics textbook. It covers exploring and organizing data using frequency distributions, histograms, graphs, scatterplots, and other methods. The objectives are to organize data using frequency distributions and represent data graphically. It defines key terms like population, sample, parameter, and statistic. It also describes procedures for constructing frequency distributions and calculating cumulative frequencies. Examples are provided to demonstrate how to organize various data sets into frequency distributions.
This section expands on frequency distributions by discussing additional features: midpoints, which are the averages of class limits; relative frequency, which shows what portion of the data falls in each class; and cumulative frequency, which is the running total of all previous classes' frequencies. It provides an example calculating these values for a given
This document provides an overview of descriptive statistics concepts and methods. It discusses numerical summaries of data like measures of central tendency (mean, median, mode) and variability (standard deviation, variance, range). It explains how to calculate and interpret these measures. Examples are provided to demonstrate calculating measures for sample data and interpreting what they say about the data distribution. Frequency distributions and histograms are also introduced as ways to visually summarize and understand the characteristics of data.
The document discusses tabulation and its importance. It provides objectives and rules for tabulating data, including arranging it logically and including totals. It describes key parts of an ideal table like the title, columns, body, and sources. Different types of tabulation are covered, including simple, grouped, and cross tabulation. Grouped frequency tables involve dividing a range into class intervals. Cumulative frequency tables show the sum of frequencies up to a level. Cross tabulation allows comparison of how respondents answered two questions.
Similar to first lecture to elementary statistcs (20)
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
first lecture to elementary statistcs
1. Course Title: General Statistics
Course Code: Math 161T
Programs in (College of Sciences +College of Computer
Sciences and Information + College of business and
administration)
Course coordinator: Dr. Wafa Alfawzan
Associate Professor; Department of Mathematical
Sciences, College of Science, Princess Nourah bint
Abdulrahman University
2. 1- Larson, R., Farber, B., Elementary Statistics-Picturing
the world, 5th Ed.
2- Walpole, R. E., Myers, R. H., and S. L. Myers (2007),
Probability and Statistics for Engineers and Scientists, 8th
ed., Prentice-Hall, inc., Upper Saddle River, new Jersey.
References
3. My Rules
•Listen Carefully
•Do not talk with your friend in class.
•Raise your hand for asking.
•Do not be late for lecture.
4. 1)The definition of statistics.
2) How to distinguish between a population and a sample.
3) How to distinguish between qualitative data and quantitative data.
4) How to distinguish between descriptive statistics and inference
statistics.
5) How to construct a frequency distribution including limits,
midpoints, relative frequencies, Percentage frequency table,
cumulative frequency table.
WHAT YOU SHOULD
LEARN?
5. The reasons for the appearance of Statistics:
• Census community.
• Inventory of the wealth of individuals.
• Data on births, deaths and production and consumption.
Introduction to Statistics
11. Data consist of information coming from
observations, counts, measurement, or
responses. The singular for data is datum.
Definition of data
12. Statistics is the science of collecting, organizing,
analyzing, and interpreting data in order to make
decisions.
Definition statistics
There are two types of data sets you will use when studying
statistics. These data sets are called populations and samples.
13. A population In statistics,
population is the collection of all outcomes, responses,
measurements, or counts that are of interest. For
example, if we are studying the weight of adult
women, the population is the set of weights of all the
women in the world.
Definition of a population (p.g. 3 Larson and Farber)
14. A sample is a subset, or part, of a population. In
order to use statistics to learn things about the
population.
Definition of a sample (p.g. 3 Larson and
Farber)
15. 1. bring the population to a manageable number
2. To reduce cost.
3. To help in minimizing error from the despondence
due to large number in the population.
4. Sampling helps the researcher to save time.
Reasons for drawing a sample, rather than
study a population
16. 1. A parameter is a numerical description of a
population characteristic.
2. A statistic is a numerical description of a sample
characteristic.
Note: It is important to note that a sample statistic can
differ from sample to sample whereas a population
parameter is constant for a population.
Definition of a parameter and a
statistic (p.g. 4 Larson and Farber)
17. Descriptive statistics is the branch of statistics that
involves the organization, summarization , and display
of data.
Statistical inference is the branch of statistics that
involves using a sample to draw conclusions about a
population.
BRANCHES OF STATISTICS (p.g. 5
Larson and Farber)
18. Qualitative data consist of attributes, labels, or
nonnumerical entries.
Quantitative data consist of numerical measurements
or counts.
TYPES OF DATA (p.g. 9 Larson and
Farber)
19. Data at the nominal level of measurement are
qualitative only. Data at this level are categorized
using names, labels, or qualities. No mathematical
computations can be made at this level.
Data at the ordinal level of measurement are
qualitative or quantitative. Data at this level can be
arranged in order, or ranked, but differences
between data entries are not meaningful.
LEVELS OF MEASUREMENT(p.g. 10
Larson and Farber)
20. Data at the interval level of measurement can be ordered, and
meaningful differences between data entries can be calculated.
At the interval level, a zero entry simply represents a position
on a scale; the entry is not an inherent zero.
Data at the ratio level of measurement are similar to data at
the interval level, with the added property that a zero entry is
an inherent zero. A ratio of two data values can be formed so
that one data value can be meaningfully expressed as a
multiple of another.
LEVELS OF MEASUREMENT(p.g. 10
Larson and Farber)
21. An inherent zero is a zero that implies “none.” For
instance, the amount of money you have in a savings
account could be zero dollars. In this case, the zero
represents no money; it is an inherent zero. On the
other hand, a temperature of does not represent a
condition in which no heat is present.
The temperature is simply a position on the Celsius
scale; it is not an inherent zero
24. We will learn how to create:
•Frequency table.
•Relative frequency table.
•Percentage frequency table.
•Cumulative frequency table.
Organization Data
25. A frequency distribution is a table that shows classes
or intervals of data entries with a count of the number
of entries in each class. The frequency f of a class is a
number of data entries in the class.
Definition of frequency table
(frequency distribution) (p.g. 38
Larson and Farber)
26. 250 150 250 325 70 350 200 400 130 90
130 300 450 160 200 59 130 150 270 275
150 170 180 95 250 200 400 200 100 220
Example 1 p.g. 39 Larson and Farber :
The following sample data set lists the prices (in dollars) of 30
portable global positioning system (GPS) navigators. Construct
a frequency distribution that has seven classes.
27. For large samples, we can’t use the simple frequency
table to represent the data.
We need to divide the data into groups or intervals or
classes.
So, we need to determine:
•First step :the number of intervals (k).
•Second step :the range (R).
•Third step :the Width of the interval (w).
Frequency distribution for quantitative data
28. A small number of intervals are not good because
information will be lost.
A large number of intervals are not helpful to
summarize the data.
A commonly followed rule is that 5 k 20
We select 7 intervals in our example.
The number of intervals (k)
29. It is the difference between the maximum and the
minimum observation (entries) in the data set.
R = the maximum entry - the minimum entry
The range (R)
30. 250 150 250 325 70 350 200 400 130 90
130 300 450 160 200 59 130 150 270 275
150 170 180 95 250 200 400 200 100 220
Example 1 p.g. 39 Larson and Farber :
The following sample data set lists the prices (in dollars) of
30 portable global positioning system (GPS) navigators.
Construct a frequency distribution that has seven classes.
31. Find the class width as follows. Determine the range of
the data, divide the range by the number of classes, and
round up to the next convenient number.
Class intervals generally should be of the same width.
W=391/7 = 55.86 Round up to 56.
The Width of the interval (w)
32. Forth step:
Choose the minimum observation to be the lower limit of the
first interval and add the width of interval to get the lower
limit of the second interval and so on
the lower limit of the second interval
59+56=115
the lower limit of the third interval
115+56=171
the lower limit of the fourth interval
171+56=227
the lower limit of the fifth interval
227+56=283
the lower limit of the sixth interval
283+56=339
the lower limit of the seventh interval
339+56=395
33. Fifth step:
The upper limit of the first class is one less than the lower limit of the second
class.
the upper limit of second interval 171-1=170
the upper limit of third interval 227-1=226
the upper limit of fourth interval 283-1=282
the upper limit of first interval 115-1=114
the upper limit of fifth interval 339-1=338
the upper limit of sixth interval 395-1=394
the upper limit of seventh interval 394+56=450
34. frequency tally Class interval
59-114
115-170
171-226
227-282
283-338
339-394
395-450
Total
35. 250 150 250 325 70 350 200 400 130 90
130 300 450 160 200 59 130 150 270 275
150 170 180 95 250 200 400 200 100 220
Example 1 p.g. 39 Larson and Farber :
The following sample data set lists the prices (in dollars) of
30 portable global positioning system (GPS) navigators.
Construct a frequency distribution that has seven classes.
36. frequency Tally Class interval
5 |||| 59-114
115-170
171-226
227-282
283-338
339-394
395-450
Total
37. 250 150 250 325 70 350 200 400 130 90
130 300 450 160 200 59 130 150 270 275
150 170 180 95 250 200 400 200 100 220
Example 1 p.g. 39 Larson and Farber :
The following sample data set lists the prices (in dollars) of
30 portable global positioning system (GPS) navigators.
Construct a frequency distribution that has seven classes.
38. frequency Tally Class interval
5 |||| 59-114
8 |||| ||| 115-170
171-226
227-282
283-338
339-394
395-450
Total
41. the class lower boundary= the lower limit – (0.5)
the class upper boundary= the upper limit + (0.5)
Definition of the Class boundaries intervals
Class boundaries are the numbers that separate classes
without forming gaps between them. If data entries are
integers, subtract 0.5 from each lower limit to find the lower
class boundaries. To find the upper class boundaries, add 0.5
to each upper limit. The upper boundary of a class will equal
the lower boundary of the next higher class.
42. frequency class boundary Class interval
5 58.5-114.5 59-114
8 114.5-170.5 115-170
6 170.5-226.5 171-226
5 226.5-282.5 227-282
2 282.5-338.5 283-338
1 338.5-394.5 339-394
3 394.5-450.5 395-450
30 Total
43. The midpoint of a class is the sum of the lower
and upper limits of the class divided by two.
The Mid-interval (Midpoints)
=(the lower limit+ the upper limit)/2
Definition of the Mid-interval (Midpoints)
44. •
frequency midpoint class interval
5 86.5 59-114
8 142.5 115-170
6 198.5 171-226
5 254.5 227-282
2 310.5 283-338
1 366.5 339-394
3 422.5 395-450
30 Total
45. The relative frequency of a class is the portion
or percentage of the data that falls in that class.
To find the relative frequency of a class, divide
the frequency (f) by the sample size (n).
Definition of the relative frequency
46. the relative
frequency
frequency Class interval
0.17 5 59-114
0.27 8 115-170
0.2 6 171-226
0.17 5 227-282
0.07 2 283-338
0.03 1 339-394
0.1 3 395-450
1 30 Total
the relative frequency
49. The cumulative frequency of a class is the sum
of the frequencies of that class and all previous
classes. The cumulative frequency of the last
class is equal to the sample size n.
Definition of The cumulative frequency
50. Ascending cumulative frequency table
Cumulative
frequency
Frequency Class interval
5 5 59-114
13 8 115-170
19 6 171-226
24 5 227-282
26 2 283-338
27 1 339-394
30 3 395-450
30 Total
51. Find from the table:
• The Width of the interval
• The midpoints
• class boundaries
• The relative frequency of
intervals.
• The percentage frequency of
intervals.
frequency
Class
interval
100 16-20
122 21-25
900 26-30
207 31-35
795 36-40
568 41-45
322 46-50
Example
52. Summary of lecture
In these lecture we create:
•frequency table
•the percentage frequency
table
•the relative frequency table
• cumulative frequency table
53. ⮚Ex1.1, P 6: 1,2,3,4,5,6, 8 , 9, 10,11, 12, 13, 14,15,
19;
⮚Ex1.2, P 13: 7, 8, 9, 10,14, 15, 18;
⮚ Ex 2.1, P 47: 11, 12, 14, 31, 32,33.
Homework
From (Larson and Farber)