This document provides a review for a week 3 quiz in a math 221 course. It summarizes key topics covered, including data collection methods, descriptive and inferential statistics, qualitative and quantitative data, sampling techniques, populations and samples, standard deviation and variance, regression equations, correlation coefficients, stem-and-leaf plots, and frequency distributions. Examples are provided for regression equations, correlation coefficients, and constructing a stem-and-leaf plot from frequency data. Students are directed to additional resources on Facebook for further assistance.
This document provides an overview of key concepts in statistics that a student should understand, including: distinguishing between different data collection methods; descriptive and inferential statistics; qualitative and quantitative data; different sampling methods; populations and samples; standard deviation and variance; regression equations; correlation coefficients; creating stem-and-leaf plots; and identifying descriptive statistics from data sets. Examples are provided to illustrate concepts like calculating a regression equation and determining class boundaries in a frequency distribution table.
This document provides questions to guide analysis of salary data from the University of Arizona using R. It begins by introducing the Shapiro-Wilk test for normality and having the student generate normal and non-normal random test data to apply the test. It then asks students to summarize the test data, interpret Shapiro-Wilk test results, and analyze salary data from UA to determine if it is normally distributed and describe patterns in maximum and minimum salaries.
De vry math 221 all discussion+ilbs latest 2016 novemberlenasour
This document provides instructions and questions for weekly discussions and assignments for a statistics course (MATH 221). It includes discussion prompts and questions for 8 weekly topics (probability, regression, normal distributions, confidence intervals, etc.). It also provides the instructions and questions for the Week 2 in-lab assignment, which involves analyzing survey data in Excel and interpreting graphs and descriptive statistics.
De vry math 221 all discussion+ilbs latest 2016 november 1lenasour
This document provides discussion questions and assignments for DeVry MATH 221 Statistics for Decision Making for weeks 1 through 7 from November 2016. It includes weekly discussion prompts on topics like helpful course resources, regression equations, probability, discrete random variables, and normal distributions. It also provides instructions and questions for weekly interactive labs covering statistical concepts like descriptive statistics, probability, binomial distributions, and confidence intervals. Students are asked to find and report means, standard deviations, probabilities, and create graphs to analyze and summarize sample data. The document is a compilation of material to help students learn and be evaluated on key statistics topics.
De vry math 221 all ilabs latest 2016 novemberlenasour
This document contains instructions for completing a statistics lab assignment involving analyzing data from a student survey. The lab includes creating graphs in Excel, calculating descriptive statistics, finding probabilities and confidence intervals, and comparing distributions. Students are asked to paste graphs, calculate measures like means and standard deviations, and answer questions interpreting their results in short paragraphs. The document provides statistical concepts and formulas to guide the analysis.
De vry math221 all ilabs latest 2016 novemberlenasour
This document provides instructions for completing a statistics lab assignment involving analyzing data from a student survey. The lab involves creating graphs in Excel, calculating descriptive statistics, and finding confidence intervals. Students are asked to calculate measures like means, standard deviations, and binomial probabilities for variables measuring things like student heights, money, time spent watching TV, and coin flip results. Confidence intervals are found for sleep hours and heights by gender.
This document provides a review for a week 3 quiz in a math 221 course. It summarizes key topics covered, including data collection methods, descriptive and inferential statistics, qualitative and quantitative data, sampling techniques, populations and samples, standard deviation and variance, regression equations, correlation coefficients, stem-and-leaf plots, and frequency distributions. Examples are provided for regression equations, correlation coefficients, and constructing a stem-and-leaf plot from frequency data. Students are directed to additional resources on Facebook for further assistance.
This document provides an overview of key concepts in statistics that a student should understand, including: distinguishing between different data collection methods; descriptive and inferential statistics; qualitative and quantitative data; different sampling methods; populations and samples; standard deviation and variance; regression equations; correlation coefficients; creating stem-and-leaf plots; and identifying descriptive statistics from data sets. Examples are provided to illustrate concepts like calculating a regression equation and determining class boundaries in a frequency distribution table.
This document provides questions to guide analysis of salary data from the University of Arizona using R. It begins by introducing the Shapiro-Wilk test for normality and having the student generate normal and non-normal random test data to apply the test. It then asks students to summarize the test data, interpret Shapiro-Wilk test results, and analyze salary data from UA to determine if it is normally distributed and describe patterns in maximum and minimum salaries.
De vry math 221 all discussion+ilbs latest 2016 novemberlenasour
This document provides instructions and questions for weekly discussions and assignments for a statistics course (MATH 221). It includes discussion prompts and questions for 8 weekly topics (probability, regression, normal distributions, confidence intervals, etc.). It also provides the instructions and questions for the Week 2 in-lab assignment, which involves analyzing survey data in Excel and interpreting graphs and descriptive statistics.
De vry math 221 all discussion+ilbs latest 2016 november 1lenasour
This document provides discussion questions and assignments for DeVry MATH 221 Statistics for Decision Making for weeks 1 through 7 from November 2016. It includes weekly discussion prompts on topics like helpful course resources, regression equations, probability, discrete random variables, and normal distributions. It also provides instructions and questions for weekly interactive labs covering statistical concepts like descriptive statistics, probability, binomial distributions, and confidence intervals. Students are asked to find and report means, standard deviations, probabilities, and create graphs to analyze and summarize sample data. The document is a compilation of material to help students learn and be evaluated on key statistics topics.
De vry math 221 all ilabs latest 2016 novemberlenasour
This document contains instructions for completing a statistics lab assignment involving analyzing data from a student survey. The lab includes creating graphs in Excel, calculating descriptive statistics, finding probabilities and confidence intervals, and comparing distributions. Students are asked to paste graphs, calculate measures like means and standard deviations, and answer questions interpreting their results in short paragraphs. The document provides statistical concepts and formulas to guide the analysis.
De vry math221 all ilabs latest 2016 novemberlenasour
This document provides instructions for completing a statistics lab assignment involving analyzing data from a student survey. The lab involves creating graphs in Excel, calculating descriptive statistics, and finding confidence intervals. Students are asked to calculate measures like means, standard deviations, and binomial probabilities for variables measuring things like student heights, money, time spent watching TV, and coin flip results. Confidence intervals are found for sleep hours and heights by gender.
This document provides practice problems for students to work on fraction skills aligned to different math standards. It includes 5 problems for each standard to allow teachers to select the appropriate level of challenge. The problems cover explaining equivalent fractions using visual models, comparing fractions, generating and recognizing equivalent fractions, solving word problems involving fractions, and using number lines to represent fractions. The document is intended to help reteach fractions concepts based on student data.
1) Students will collect data on favorite fast foods in their class and grade to create bar graphs. They will write a report sharing their findings.
2) Fraction concepts are explored through examples of parts of wholes, such as one-third and two-sixths being equivalent.
3) A problem solving activity involves arranging digits 1-9 in groups so the sum is the same in each group, with discussion of multiple solutions.
The document discusses calculating the mean, median, and mode of data sets. It provides examples of how to calculate the mean by adding all values and dividing by the number of values. The median is found by ordering values and selecting the middle value or averaging the two middle values. The mode is the most frequently occurring value. Examples are given of calculating each measure for various data sets.
This document discusses different probability concepts like factorials, permutations, and combinations. It provides examples of calculating each using Excel functions like FACT, PERMUT, and COMBIN. It also covers permutations and combinations when items are identical. Permutations calculate arrangements where order matters, while combinations do not consider order. The document aims to demonstrate how to easily calculate these probabilities in Excel.
Here are the steps to find the mean of the data set:
1) List the data: 4, 6, 8, 10, 12
2) Find the sum of the data: 4 + 6 + 8 + 10 + 12 = 40
3) Count the number of data points: There are 5 data points
4) Divide the sum by the count: 40/5 = 8
Therefore, the mean of the data set is 8.
This document outlines an activity to practice modeling and predicting values using simple linear regression. Students are asked to:
1. Record guesses and actual values for various jars of jelly beans to see how off their guesses are.
2. Use the differences between guesses and actuals to develop a formula to "correct" future guesses.
3. Apply the same process to guessing college football wins to refine their predictive model.
4. Complete tables and calculations in StatCrunch to fit linear and quadratic models to their data and evaluate which model fits best. They are asked to use the model to predict further values and evaluate residuals.
De vry math 399 ilabs & discussions latest 2016lenasour
This document provides information and discussion questions for several weeks of a statistics course (MATH 399) at DeVry University. It includes discussion questions and assignments related to topics like descriptive statistics, regression, probability, confidence intervals, and hypothesis testing. For each week, it provides the discussion question, any relevant instructions, and sometimes a short summary of the statistical concept being covered. It also includes information about completing iLabs (interactive labs) and assignments in Excel to reinforce these statistical topics.
De vry math 399 ilabs & discussions latest 2016 novemberlenasour
This document provides materials and instructions for several weekly discussions and iLabs for a DeVry University MATH 399 course. It includes discussion prompts and questions for weeks 1 through 7 on topics such as descriptive statistics, regression, probability, confidence intervals, and hypothesis testing. It also provides instructions and questions for iLabs on related statistical concepts involving Excel, probability distributions, descriptive statistics, and confidence intervals. Students are asked to perform calculations, create graphs and charts, interpret results, and answer questions demonstrating their understanding of the statistical content.
This document provides materials for a 4th grade mathematics unit. It includes lessons, activities, practice problems and games related to fractions, data analysis, geometry, measurement, number sense, and problem solving. Some key lessons include dividing objects into halves or other fractions, collecting and graphing data, solving word problems, and playing numerical games like a dice game called "Corn Shucks." The document offers guidance for teachers on discussing concepts and assessing student understanding.
This document contains several statistics problems and questions for students to work through. It includes questions about calculating relative frequencies from data, finding mean, median, mode and other measures of center from data sets, determining what score is needed on a final exam to achieve a certain overall average, and other common statistics problems. The document provides data and instructions for students to analyze the data and answer the questions.
This chapter introduces how to analyze simple questionnaires using SPSS. It will teach how to structure datasets for computer analysis, perform basic statistical techniques to describe data patterns, and display data visually. The chapter uses a lateralization questionnaire as a sample dataset to demonstrate simple analyses. It describes how to code questionnaire responses numerically and organize the data in a matrix format with rows for participants and columns for questions. This initial dataset captures patterns of right or left laterality preferences among the first six participants.
- The document discusses key concepts related to analyzing and operating on data including fractions, including constructing and interpreting stem-and-leaf plots, adding and subtracting fractions, understanding how sample size affects results, finding common denominators, and converting between fractions, decimals, and percents.
- It provides examples and instructions for creating stem-and-leaf plots to organize data, adding and subtracting fractions by finding common denominators, and how sample size can impact experimental versus theoretical probabilities.
- Additional concepts covered include finding data landmarks like maximum, minimum and range from a data set, and converting between fractions, decimals, and percentages.
The document provides instructions for a PSSA review for 8th grade mathematics that includes vocabulary reviews, practice exercises, and solutions for key math concepts tested. It outlines the five categories covered - Numbers & Operations, Measurement, Geometry, Algebraic Concepts, and Data Analysis & Probability - and provides sources for the content. Students are directed to work through the review independently using the provided answer keys and online reinforcement resources.
The document defines and provides examples for calculating the mean, median, and mode of data sets. It explains that the mode is the most common number, the median is the middle number once the data is ordered, and the mean is the average found by adding all numbers and dividing by the count. It then provides 6 practice data sets and asks the reader to calculate the mean, median, and mode of each, showing their work.
1. The document discusses decision trees, bagging, and random forests. It provides an overview of how classification and regression trees (CART) work using a binary tree data structure and recursive data partitioning. It then explains how bagging generates diverse trees by bootstrap sampling and averages the results. Finally, it describes how random forests improve upon bagging by introducing random feature selection to generate less correlated and more accurate trees.
1.MATH 221 Statistics for Decision MakingWeek 2 iLabName.docxAlyciaGold776
This document provides instructions for a statistics lab assignment. Students are asked to analyze survey data provided in an Excel spreadsheet. The assignment involves creating graphs in Excel, including a pie chart for car color, a histogram for heights, and a stem-and-leaf plot for money. Students then calculate descriptive statistics for heights by gender and answer questions interpreting the graphs and statistics.
Data Science Interview Questions | Data Science Interview Questions And Answe...Simplilearn
This video on Data science interview questions will take you through some of the most popular questions that you face in your Data science interviews. It’s simply impossible to ignore the importance of data and our capacity to analyze, consolidate, and contextualize it. Data scientists are relied upon to fill this need, but there is a serious dearth of qualified candidates worldwide. If you’re moving down the path to be a data scientist, you need to be prepared to impress prospective employers with your knowledge. In addition to explaining why data science is so important, you’ll need to show that you're technically proficient with Big Data concepts, frameworks, and applications. So, here we discuss the list of most popular questions you can expect in an interview and how to frame your answers.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. The data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
Learn more at www.simplilearn.com/big-data-and-analytics/python-for-data-science-training
Important Terminologies In Statistical Inference I IZoha Qureshi
This document provides an introduction to classifying data through frequency distributions and histograms. It explains that raw data needs to be processed into meaningful information to help managers draw the right conclusions. Frequency distributions summarize raw data by arranging it into classes and recording the frequencies. Guidelines are provided for constructing frequency distribution tables, including identifying the minimum and maximum values, deciding on the number of classes, determining the class width, and formulating class boundaries. Histograms are then described as a graphical representation of a frequency distribution using bars to depict the frequencies within each class. Cumulative frequency distributions and ogive curves are also introduced as ways to represent cumulative totals of the frequencies.
This document provides examples for a statistics lab involving random variables, probability distributions, and confidence intervals.
[1] It asks whether rolling a die is a discrete or continuous random variable and to calculate the mean and standard deviation of rolling a 4-sided die.
[2] The mean of rolling the 4-sided die is calculated as 2.5 and the standard deviation is calculated as 1.118.
[3] It then has the student calculate descriptive statistics like the mean and median of various data samples and compares how centered they are around the true parameter.
The document provides examples and step-by-step instructions for conducting linear regression analyses in Minitab. It discusses how to find confidence intervals for slopes, interpret regression results, make predictions based on regression equations, and conduct hypothesis tests regarding the significance of regression slopes. For example 4, the null hypothesis is that the slope β1 equals 0, indicating crossword puzzle success and jelly beans consumed are not linearly related, while the alternative is that β1 does not equal 0, meaning they are linearly related. The t-statistic is 1.93490422105 and the p-value is 0.075, so the null cannot be rejected at the 0.10 significance level.
This document provides examples for 3 homework problems from a statistics class. Problem 18 demonstrates how to calculate the range, mean, variance, and standard deviation of a data set using Minitab software. Problem 22 shows how to identify the minimum, maximum, quartiles, and interquartile range from a box and whisker plot. Problem 24 matches z-scores to a histogram by considering their relationship to the mean and standard deviation.
This document provides practice problems for students to work on fraction skills aligned to different math standards. It includes 5 problems for each standard to allow teachers to select the appropriate level of challenge. The problems cover explaining equivalent fractions using visual models, comparing fractions, generating and recognizing equivalent fractions, solving word problems involving fractions, and using number lines to represent fractions. The document is intended to help reteach fractions concepts based on student data.
1) Students will collect data on favorite fast foods in their class and grade to create bar graphs. They will write a report sharing their findings.
2) Fraction concepts are explored through examples of parts of wholes, such as one-third and two-sixths being equivalent.
3) A problem solving activity involves arranging digits 1-9 in groups so the sum is the same in each group, with discussion of multiple solutions.
The document discusses calculating the mean, median, and mode of data sets. It provides examples of how to calculate the mean by adding all values and dividing by the number of values. The median is found by ordering values and selecting the middle value or averaging the two middle values. The mode is the most frequently occurring value. Examples are given of calculating each measure for various data sets.
This document discusses different probability concepts like factorials, permutations, and combinations. It provides examples of calculating each using Excel functions like FACT, PERMUT, and COMBIN. It also covers permutations and combinations when items are identical. Permutations calculate arrangements where order matters, while combinations do not consider order. The document aims to demonstrate how to easily calculate these probabilities in Excel.
Here are the steps to find the mean of the data set:
1) List the data: 4, 6, 8, 10, 12
2) Find the sum of the data: 4 + 6 + 8 + 10 + 12 = 40
3) Count the number of data points: There are 5 data points
4) Divide the sum by the count: 40/5 = 8
Therefore, the mean of the data set is 8.
This document outlines an activity to practice modeling and predicting values using simple linear regression. Students are asked to:
1. Record guesses and actual values for various jars of jelly beans to see how off their guesses are.
2. Use the differences between guesses and actuals to develop a formula to "correct" future guesses.
3. Apply the same process to guessing college football wins to refine their predictive model.
4. Complete tables and calculations in StatCrunch to fit linear and quadratic models to their data and evaluate which model fits best. They are asked to use the model to predict further values and evaluate residuals.
De vry math 399 ilabs & discussions latest 2016lenasour
This document provides information and discussion questions for several weeks of a statistics course (MATH 399) at DeVry University. It includes discussion questions and assignments related to topics like descriptive statistics, regression, probability, confidence intervals, and hypothesis testing. For each week, it provides the discussion question, any relevant instructions, and sometimes a short summary of the statistical concept being covered. It also includes information about completing iLabs (interactive labs) and assignments in Excel to reinforce these statistical topics.
De vry math 399 ilabs & discussions latest 2016 novemberlenasour
This document provides materials and instructions for several weekly discussions and iLabs for a DeVry University MATH 399 course. It includes discussion prompts and questions for weeks 1 through 7 on topics such as descriptive statistics, regression, probability, confidence intervals, and hypothesis testing. It also provides instructions and questions for iLabs on related statistical concepts involving Excel, probability distributions, descriptive statistics, and confidence intervals. Students are asked to perform calculations, create graphs and charts, interpret results, and answer questions demonstrating their understanding of the statistical content.
This document provides materials for a 4th grade mathematics unit. It includes lessons, activities, practice problems and games related to fractions, data analysis, geometry, measurement, number sense, and problem solving. Some key lessons include dividing objects into halves or other fractions, collecting and graphing data, solving word problems, and playing numerical games like a dice game called "Corn Shucks." The document offers guidance for teachers on discussing concepts and assessing student understanding.
This document contains several statistics problems and questions for students to work through. It includes questions about calculating relative frequencies from data, finding mean, median, mode and other measures of center from data sets, determining what score is needed on a final exam to achieve a certain overall average, and other common statistics problems. The document provides data and instructions for students to analyze the data and answer the questions.
This chapter introduces how to analyze simple questionnaires using SPSS. It will teach how to structure datasets for computer analysis, perform basic statistical techniques to describe data patterns, and display data visually. The chapter uses a lateralization questionnaire as a sample dataset to demonstrate simple analyses. It describes how to code questionnaire responses numerically and organize the data in a matrix format with rows for participants and columns for questions. This initial dataset captures patterns of right or left laterality preferences among the first six participants.
- The document discusses key concepts related to analyzing and operating on data including fractions, including constructing and interpreting stem-and-leaf plots, adding and subtracting fractions, understanding how sample size affects results, finding common denominators, and converting between fractions, decimals, and percents.
- It provides examples and instructions for creating stem-and-leaf plots to organize data, adding and subtracting fractions by finding common denominators, and how sample size can impact experimental versus theoretical probabilities.
- Additional concepts covered include finding data landmarks like maximum, minimum and range from a data set, and converting between fractions, decimals, and percentages.
The document provides instructions for a PSSA review for 8th grade mathematics that includes vocabulary reviews, practice exercises, and solutions for key math concepts tested. It outlines the five categories covered - Numbers & Operations, Measurement, Geometry, Algebraic Concepts, and Data Analysis & Probability - and provides sources for the content. Students are directed to work through the review independently using the provided answer keys and online reinforcement resources.
The document defines and provides examples for calculating the mean, median, and mode of data sets. It explains that the mode is the most common number, the median is the middle number once the data is ordered, and the mean is the average found by adding all numbers and dividing by the count. It then provides 6 practice data sets and asks the reader to calculate the mean, median, and mode of each, showing their work.
1. The document discusses decision trees, bagging, and random forests. It provides an overview of how classification and regression trees (CART) work using a binary tree data structure and recursive data partitioning. It then explains how bagging generates diverse trees by bootstrap sampling and averages the results. Finally, it describes how random forests improve upon bagging by introducing random feature selection to generate less correlated and more accurate trees.
1.MATH 221 Statistics for Decision MakingWeek 2 iLabName.docxAlyciaGold776
This document provides instructions for a statistics lab assignment. Students are asked to analyze survey data provided in an Excel spreadsheet. The assignment involves creating graphs in Excel, including a pie chart for car color, a histogram for heights, and a stem-and-leaf plot for money. Students then calculate descriptive statistics for heights by gender and answer questions interpreting the graphs and statistics.
Data Science Interview Questions | Data Science Interview Questions And Answe...Simplilearn
This video on Data science interview questions will take you through some of the most popular questions that you face in your Data science interviews. It’s simply impossible to ignore the importance of data and our capacity to analyze, consolidate, and contextualize it. Data scientists are relied upon to fill this need, but there is a serious dearth of qualified candidates worldwide. If you’re moving down the path to be a data scientist, you need to be prepared to impress prospective employers with your knowledge. In addition to explaining why data science is so important, you’ll need to show that you're technically proficient with Big Data concepts, frameworks, and applications. So, here we discuss the list of most popular questions you can expect in an interview and how to frame your answers.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. The data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
Learn more at www.simplilearn.com/big-data-and-analytics/python-for-data-science-training
Important Terminologies In Statistical Inference I IZoha Qureshi
This document provides an introduction to classifying data through frequency distributions and histograms. It explains that raw data needs to be processed into meaningful information to help managers draw the right conclusions. Frequency distributions summarize raw data by arranging it into classes and recording the frequencies. Guidelines are provided for constructing frequency distribution tables, including identifying the minimum and maximum values, deciding on the number of classes, determining the class width, and formulating class boundaries. Histograms are then described as a graphical representation of a frequency distribution using bars to depict the frequencies within each class. Cumulative frequency distributions and ogive curves are also introduced as ways to represent cumulative totals of the frequencies.
This document provides examples for a statistics lab involving random variables, probability distributions, and confidence intervals.
[1] It asks whether rolling a die is a discrete or continuous random variable and to calculate the mean and standard deviation of rolling a 4-sided die.
[2] The mean of rolling the 4-sided die is calculated as 2.5 and the standard deviation is calculated as 1.118.
[3] It then has the student calculate descriptive statistics like the mean and median of various data samples and compares how centered they are around the true parameter.
Similar to Some tricky things math 221 week 3 (20)
The document provides examples and step-by-step instructions for conducting linear regression analyses in Minitab. It discusses how to find confidence intervals for slopes, interpret regression results, make predictions based on regression equations, and conduct hypothesis tests regarding the significance of regression slopes. For example 4, the null hypothesis is that the slope β1 equals 0, indicating crossword puzzle success and jelly beans consumed are not linearly related, while the alternative is that β1 does not equal 0, meaning they are linearly related. The t-statistic is 1.93490422105 and the p-value is 0.075, so the null cannot be rejected at the 0.10 significance level.
This document provides examples for 3 homework problems from a statistics class. Problem 18 demonstrates how to calculate the range, mean, variance, and standard deviation of a data set using Minitab software. Problem 22 shows how to identify the minimum, maximum, quartiles, and interquartile range from a box and whisker plot. Problem 24 matches z-scores to a histogram by considering their relationship to the mean and standard deviation.
This document provides instructions for finding recorded lectures in iConnect Live for Math 221 and Math 533 courses. It explains that the user should click the Week 1 button, then the iConnect Live link, be patient as the recording loads, select the proper course and click show, then launch the desired lecture. It notes that the lecture slides may not be visible initially but will load during the lecture. It also provides instructions for downloading and navigating the lecture recording.
This presentation provides help on numbers 13, 15 and 19 on the Week 7 Homework. This contains hypothesis testing examples for 1 Sample z, 1 Sample t and 1 proportion.
This document provides step-by-step instructions for completing homework problems related to hypothesis testing using z-tests. It includes instructions for finding critical values, performing left-tailed, right-tailed, and two-tailed z-tests using Minitab software. Examples are provided for problems testing claims about population means, finding test statistics, determining p-values, and interpreting results to either reject or fail to reject the null hypothesis. Guidance is given to carefully consider the wording of claims and hypotheses and set up tests accordingly.
Help on funky proportion confidence interval questionsBrent Heard
This presentation provides an alternate way of getting confidence intervals for proportions. We have at least one problem in Week 6 where this applies. Rather than using Minitab, I have an Excel template that will help. Instructions on obtaining the file are at the end of the presentation.
Using minitab instead of tables for z values probabilities etcBrent Heard
This document discusses using Minitab instead of tables to find probabilities and z-values for the standard normal distribution. It provides examples of finding probabilities for given z-values using both tables and Minitab, and shows that Minitab makes the calculations faster and easier. The document also demonstrates how to use Minitab to find z-values for given probabilities, as well as find the z-values that define a symmetric probability between them. Overall, the document promotes using Minitab over tables for standard normal distribution calculations.
This presentation describes choosing the right options in Minitab for distributions related to the "tail" of the distribution. I cover Binomial, Poisson and the Geometric Distributions.
Help on binomial problems using minitabBrent Heard
The document provides help on solving binomial probability problems using Minitab software. It explains how to calculate the probabilities of exactly 8 successes, at least 8 successes, and less than 8 successes when randomly sampling 10 men and the probability of any one man being a basketball fan is 49%. The key steps are to use Minitab's binomial distribution function, enter the number of trials (10), probability of success (0.49), and use the shaded area tab to calculate the probabilities by selecting the left, right, or middle tail as appropriate. The probabilities calculated are 0.03890 for exactly 8, 0.04800 for at least 8, and 0.9520 for less than 8.
This document provides a summary of key concepts and example problems to help students prepare for their undergraduate statistics final exam. It covers topics like levels of measurement, types of sampling, descriptive statistics, populations and samples, qualitative vs. quantitative data, pivot tables, normal distributions, Poisson distributions, and confidence intervals. The examples are worked out step-by-step to demonstrate the calculations and show the reasoning behind each answer. The goal is to help refresh students' memories on what they learned and to feel more prepared for their upcoming final.
This document provides examples for homework problems 17, 18, and 20 from Week 6. Example 17 constructs a 95% confidence interval for the proportion of men who wear hats using survey data. Example 18 calculates sample sizes needed for estimating a population proportion within a margin of error. Example 20 constructs 95% confidence intervals for the proportions of adults who report traffic congestion as a problem in different regions, based on survey data.
This document provides examples for homework problems assigned in Week 5. It includes step-by-step work and explanations for problems involving normal distributions, z-scores, percentiles, and sampling distributions. The examples demonstrate how to use Minitab to find probabilities and critical values for normally distributed data. Key concepts covered include interpreting left and right tails, shifting to standardized units, and adjusting standard deviations for sampling distributions.
This document provides examples and solutions for statistics homework problems using binomial, geometric, and Poisson distributions in Minitab software. It addresses three homework problems on finding probabilities for the number of households reporting they feel secure, the number of sales calls required, and the number of hurricanes hitting an island. Step-by-step instructions are given for setting up each problem in Minitab and calculating the requested probabilities. None of the probabilities calculated are described as unusual.
This document contains step-by-step instructions from a statistics professor on solving various probability and counting problems that commonly appear on homework assignments. The professor demonstrates how to calculate combinations, probabilities, means, variances, and standard deviations using both calculators and Excel. Examples include finding the number of combinations of letters in words, the probability of certain race outcomes, and describing the properties of probability distributions.
This document provides examples for additional homework problems in a statistics course. It discusses problems similar to numbers 11, 13, and 14 from the homework. For problem 11, it explains how to match a regression equation to the correct graph by examining the slope and y-intercept. For problem 13, it demonstrates how to calculate the coefficient of determination from the linear correlation coefficient. Finally, for problem 14 it works through an example of using a multiple regression equation to predict GPA based on given high school GPA and college board scores.
Week 1 Statistics for Decision (3x9 on Wednesday)Brent Heard
This document provides examples and explanations for 3 extra homework problems from a statistics class.
(1) The first problem asks students to find the range, mean, variance and standard deviation for a sample data set using Minitab software. Step-by-step instructions are given.
(2) The second problem involves interpreting parts of a box-and-whisker plot like minimum, maximum, and quartiles.
(3) The third problem has students match z-scores to points on a histogram by considering where values above, below, or at the mean would fall. An unusual z-score is identified as well.
The document contains statistics lab report scores for 8 students who spent varying amounts of time preparing. It includes the regression equation relating hours spent to score and predicts a score for someone who spent 1 hour. It also defines the correlation coefficient and explains it measures the strength of the linear relationship between two variables.
- The document provides an overview of topics that may be covered on the Math 533 final exam, including hypothesis testing, the binomial distribution, descriptive statistics, confidence intervals, and regression analysis.
- It includes examples of sample questions and worked problems for each topic to help students prepare.
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
Session 1 - Intro to Robotic Process Automation.pdf
Some tricky things math 221 week 3
1. B Heard
(Do not copy or post without my
permission, students may download one
copy for personal use)
2. Let’s look at two questions
The salaries of 7 randomly selected employees at
Acme Inc. (in thousands) are listed as:
27,33,35,39,42,46,51
John Doe’s salary for the first 7 years of his
career at Acme Inc (in thousands) was as follows:
27,33,35,39,42,46,51
Onthe chart that follows, there will be
questions about each of these.
3. For both cases, do the following
1. Stem and leaf plot
2. Find the mean
3. Find the median
4. Identify the mode
5. Find the range
6. Find the variance
7. Find the standard deviation
5. • The salaries of 7 randomly selected employees at
Acme Inc. (in thousands) are listed as:
27,33,35,39,42,46,51
Not a problem on this one because this is
“SAMPLE DATA”
We can use Minitab
7. For the stem and leaf plot, I simply used
Graph >> Stem and Leaf (selecting my data for graph
variable)
This is not correct (there are two 3
stems and two 4 stems), so I went
back in to see what caused this. I
discovered I needed to put in a
value of “10” for the increment
because I want the stems to be the
“tens digit.”
8. After I did this, I got the correct stem and leaf
9. Honestly,I like to type out my stem and leaf plots
when I am dealing with a small amount of data
(10 or less)
2| 7
3| 359
4| 26
5| 1
10. This is the first time I’ve ever caught this. I’m
sure there is a reason, but I think I figured out
a quick fix (that I explained to you)
I still recommend just doing it by hand! Most,
if not ALL of the problems you will see on
quizzes and the final exam can EASILY BE DONE
BY HAND.
Always count your “leaves” and make sure you have
the same number as you do data points
11. As for the remainder of the question, I used
Minitab for the FIRST ONE.
Use Stat >> Display Descriptive Statistics
12. Click Statistics >> /Display Descriptive Statistics
Select your data
Click “Statistics button” (chart that follows)
14. Let’s answer the questions
1. Stem and leaf plot (Done)
2. Find the mean (39.0)
3. Find the median (39.0)
4. Identify the mode (No Mode – the * means there isn’t one)
5. Find the range (24)
6. Find the variance (66.33)
7. Find the standard deviation (8.14)
15. You just need to know the buttons to push!
Let’s look at the second question.
17. • John Doe’s salary for the first 7 years of his career
at Acme Inc (in thousands) was as follows:
27,33,35,39,42,46,51
THIS IS A POPULATION SINCE IT IS THE FIRST 7
YEARS OF HIS CAREER (WE HAVE ALL OF THE
DATA)
We can use Minitab for everything except the
Standard Deviation and Variance
18. 1. Stem and leaf plot
2. Find the mean
3. Find the median
4. Identify the mode
5. Find the range
6. Find the variance
7. Find the standard deviation
19. Stem and leaf – The same as the previous
since the data is the same
2| 7
3| 359
4| 26
5| 1
20. The other questions
NO
1. Stem and leaf plot (Done)
2. Find the mean (39.0)
3. Find the median (39.0)
4. Identify the mode (No Mode – the * means there isn’t one)
5. Find the range (24)
6. Find the variance (WRONG BECAUSE IT’S A POPULATION)
7. Find the standard deviation (WRONG BECAUSE IT’S A
POPULATION)
21. Since we are dealing with a population, the
formula is different for the variance and
standard deviation.
Minitab only gives you the SAMPLE
VARIANCE AND STANDARD DEVIATION
22. Page 81- 83 in the online text
Population formula
Divides by “N” or
the number of
data points
Sample formula
Divides by “n-1” or
“one less” than the
number of data
points
NOTE THE
DIFFERENCE IN THE
DENOMINATORS OF
THE FORMULAS
23. Thereare a couple of ways you can do this to
get the variance and standard deviation
correct
By hand using the formula from the text
Do it in Excel
24. I will show you how to do it in Excel
First, simply type your data into Excel
25. Ina different cell (below or to the right) start
typing = std (WHEN YOU DO, EXCEL WILL POP
UP A LIST OF WHAT IT THINKS YOU WANT)
26. When you have done this, select your data by
left clicking and dragging from the first data
point to the last.
27. Type a right parentheses “)” and hit enter
I see my Population Standard Deviation of 7.54
28. Similarly, I start typing “=var” and select
=VARP( to get the Population Variance of
56.857
I could have also squared 7.540368 (the
Population standard deviation) to get the
population variance
Never round until your final answer
That is why I noted 7.540368 rather than just
7.54
29. So for the second problem, I had a
Population.
If I had used my Minitab variance and
standard deviation, I would have missed the
problem
As we know now, the correct answer was
Population Standard Deviation 7.54
Population Variance 56.86
I rounded to two decimal places for both