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Simplified result of the {(3y -2 z2)3 ÷ (2x -2 y z -1)2} with positive exponents is given by
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Explanation & Answer
(3y -2 z2)3 ÷ (2x -2 y z -1)2
so : ( 27y^(-6)z^(6) ) / (4x^(-4)y^(2)z^(-2) ) so we know : A^(-1) = 1 / A
so : (27x^(4)z^(2)z^(6) ) / (y^(6)y^(2)) = (27x^(4)z^(8) ) / (y^(8)) is the correct answer write it in the paperso good luck and please if you need somthing else please i want to be the best tutor so please call me in inbox i will give you the best solution
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Purdue Global University Unit 5 Statistical Reasoning Questions
In Unit 5, there are three main topics (problem types): describing correlations, measuring correlations (with the r value) ...
Purdue Global University Unit 5 Statistical Reasoning Questions
In Unit 5, there are three main topics (problem types): describing correlations, measuring correlations (with the r value), and creating and evaluating scatterplots. You will be exposed to all three topics, and will have the opportunity to discuss and compare these topics with your fellow learners.Using any Excel dataset, choose two quantitative variables from the dataset. For example, you might choose “age” and “weight.” Next, do the following:What is the name of the dataset you have chosen? What are the names of the two quantitative variables you are investigating?Using Excel, calculate the relationship (correlation) between these two variables. Write down your calculated r value.Given the r value you calculated in number 1 above, explain what the r value tells you about the relationship between the two variables. For example, is the relationship positive or negative? Is the relationship strong, medium, or weak?Using the two variables you have chosen, create and attach to your post a scatterplot. Does the scatterplot have a linear appearance? What does the scatterplot tell you about the relationship between your two variables?Please create personalized and substantive responses to at least two other student main posts. In your response, include the following:Choose any two classmates and review their main posts.Find the dataset that each student used, and repeat their r value calculation in Excel for the variables they chose. Are they correct? Do you agree with how they described the relationship between the two variables? Explain in one paragraph and include both your results and the student results.For the same two classmates, also review their scatterplots. Discuss the “shape” of the scatterplot - is it mostly linear or is it all scattered around? Does the scatterplot seem to have a positive (upwards from left to right) or negative slope? Does their scatterplot visually match what the r value is suggesting? What can you say about the relationship based on the scatterplot?Discussion RequirementsReading and ResourcesRead the assigned chapters from the following textbooks:Bennett, J., Briggs, W.L. & Triola, M.F. (2013) Statistical Reasoning for Everyday Life (4th ed.). Upper Saddle, NJ: Pearson.Chapter 7: “Correlation and Causality”Reading the textbook and reviewing the textbook examples are excellent methods for starting each unit. Reading the textbook offers context and explanations for new concepts and methods. Completing the textbook examples on paper (and with Excel) is a great way to practice and learn the new methods and concepts introduced. Student feedback has suggested that reading the textbook and practicing the textbook examples has been particularly helpful if completed before the unit Seminar. Some students have reported that keeping a notebook handy, and recording new definitions or concepts encountered while reading is helpful, more organized, and stress reducing.This chapter includes a section that offers examples using technologies such as Excel. In addition, at the end of each chapter section, or at the end of the chapter, are review exercises that are very helpful for practicing and preparing.In this course, students may use Excel for any statistical calculations. Excel can be used to evaluate data in many ways. Excel can be used to calculate numerical measures, such as measures of center (such as mean and median) and measures of variation (variance, standard deviation, and range), as well as many other measures such as min, max, and correlation (r-value). Excel can also be used to create visualizations, such as histograms, bar graphs, pie graphs, scatterplots, and others. Excel may also be used to create linear regression equations. Because Excel is a very common tool, the Internet and YouTube both contain considerable support resources and tutorials. TEXTBOOKSBennett, J., Briggs, W., Triola, M. (2014) Statistical Reasoning: For Everyday Life.(4th ed)
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Grossmont Law Enforcement Officers Should Focus on Particular Social Groups Questions
This is the Example Prompt: The actual prompt is second.
Race vs Crime-Focus
We randomly selected 1,200 partic ...
Grossmont Law Enforcement Officers Should Focus on Particular Social Groups Questions
This is the Example Prompt: The actual prompt is second.
Race vs Crime-Focus
We randomly selected 1,200 participants form the 2020 Race IAT. Below is the two-way table for the sample with the row variable Race and the column variable Crime-Focus. Here is a description of each variable.
Race: 2020 Race- IAT participants' self-reported race.
Crime-Focus: 2020 Race-IAT participants' responses to the statement, "Law enforcement officers should pay particular attention to those social groups more heavily involved in crime, even if this means focusing on members of particular ethnic groups."
Race IAT: Race vs Crime-FocusStrongly DisagreeModerately DisagreeSlightly DisagreeNeutralSlightly
AgreeModerately
AgreeStrongly
AgreeRow TotalsAmer Indian/
Alaskan Native31101006East Asian1765130032South Asian1641120125Nat Hawaiian/
Pacific Islander30110005Black/
African Am511571101287White40018072831444121941Other/
Unknown2374445148Multiracial3463731256
EXAMPLE Neutrality about Crime-Focus
Are white IAT participants less likely than black participants to be neutral about law enforcement officers focusing their attention on particular social groups?
Work
In this question we are comparing the Black and White categories of the explanatory variable, Race. So we need to build two fractions with the totals for these explanatory variable categories in the denominators.
DenominatorsBlacksWhites?87?87?941?941The denominator is the total for Blacks.The denominator is the total for Whites.
Since we are asked about one category of the response variable, "neutral" we use the Neutral values in the numerators of the fractions.
NumeratorsBlacksWhites118711878394183941
The numerator, 11,
is "Neutral" given "Black."
The numerator, 83,
is "Neutral" given
"White."
Now we calculate and compare the percentages.
Blacks: 1187?0.126=12.6%1187?0.126=12.6%
Whites: 83941?0.088=8.8%83941?0.088=8.8%
The percentage of whites is less than the percentage of blacks. So, if the percent increase is 10% or greater, we will answer "yes" to the question, "Are white IAT participants less likely than black participants to be neutral about law enforcement officers focusing their attention on particular social groups?"
We need to calculate the percent increase from the smaller percentage to the larger percentage.
Step 1: Subtract the percentages (large - small).
12.6 - 8.8 = 3.8
Step 2: Divide the difference by the original amount and convert to a percent. Since we are calculating the percent increase from the smaller percentage to the larger percentage, the smaller percentage is the original amount.
3.88.8?0.432=43.2%3.88.8?0.432=43.2%
The percent increase is very large (more than 4 times greater than 10%), so we will answer "yes."
Answer
Approximately 8.8% of whites in the IAT sample are neutral about law enforcement officers focusing their attention on particular social groups. Whereas, approximately 12.6% of blacks are neutral. The percent increase from white participants in the IAT sample to black participants is 43.2%, so white IAT participants are less likely to be neutral than black participants.
EXAMPLE More likely to "strongly or moderately disagree"
Are black IAT participants more likely than East Asian participants to strongly or moderately disagree that law enforcement officers should focus their attention on particular social groups?
Work
In this question we are comparing the Black and East Asians categories of the explanatory variable, Race. So we need to build two fractions with the totals for these explanatory variable categories in the denominators.
DenominatorsBlacksEast Asians?87?87?32?32The denominator is the total for Blacks. The denominator is
the total for East
Asians.
We are asked about two categories of the response variable, "strongly disagree" or "moderately disagree." So, we need to add the values for each of these cells when conditioning on Black. And, we need to add the values for each of these cells when conditioning on East Asian.
NumeratorsBlacksEast Asians51+158751+158717+63217+632
The numerator,
51 + 15, is the sum of "Strongly Disagree" and "Moderately Disagree" given "Black."
The numerator,
17 + 6, is the sum
of "Strongly
Disagree" and
"Moderately
Disagree" given
"Black."
Now we calculate and compare the percentages.
Blacks: 51+1587=6687?0.759=75.9%51+1587=6687?0.759=75.9%
East Asians: 17+632=2332?0.719=71.9%17+632=2332?0.719=71.9%
The percentage of black IAt participants in the sample is greater than the percentage of East Asian participants. But, we do not yet know whether we can answer "yes" to the question, "Are black IAT participants more likely than East Asian participants to strongly or moderately disagree that law enforcement officers should focus their attention on particular social groups?"
We need to calculate the percent increase from the smaller percentage to the larger percentage.
Step 1: Subtract the percentages (large - small).
75.9 - 71.9 = 4
Step 2: Divide the difference by the original amount and convert to a percent. Since we are calculating the percent increase from the smaller percentage to the larger percentage, the smaller percentage is the original amount.
471.9?0.056=5.6%471.9?0.056=5.6%
The percent increase is too small! Since, 5.6% is less than 10%, we cannot answer "yes."
Answer
Approximately 75.9% of black IAT participants in the sample strongly or moderately disagree that law enforcement officers should focus their attention on particular social groups. Whereas, approximately 71.9% of East Asian participants strongly or moderately disagree. The percent increase from whites to East Asians is only 5.6%, so we cannot conclude that black IAT participants are more likely to strongly or moderately disagree. We randomly selected 1,200 participants form the 2020 Race IAT. Below is the two-way table for the sample with the row variable Race and the column variable Crime-Focus. Here is a description of each variable.
Actual Prompt:
We randomly selected 1,200 participants form the 2020 Race IAT. Below is the two-way table for the sample with the row variable Race and the column variable Crime-Focus. Here is a description of each variable.
Race: IAT participants' self-reported race.
Crime-Focus: IAT participants response to the statement, "Law enforcement officers should pay particular attention to those social groups more heavily involved in crime, even if this means focusing on members of particular ethnic groups."
Race IAT: Race vs Crime-Focus
Strongly DisagreeModerately DisagreeSlightly DisagreeNeutralSlightly
AgreeModerately
AgreeStrongly
AgreeRow TotalsAmer Indian/
Alaskan Native31101006East Asian1765130032South Asian1641120125Native Hawaiian/
Pacific Islander30110005Black/
African Am511571101287White40018072831444121941Other/
Unknown2374445148Multiracial3463731256
Respond to each of the following questions. Be sure to number your responses, show your work, and write your answers in context as we did on the previous page.
Are black IAT participants more likely than whites to strongly agree that law enforcement officers should focus their attention on particular social groups?
Are white IAT participants more likely than multiracial participants to moderately or strongly agree that law enforcement officers should focus their attention on particular social groups?
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Purdue Global University Unit 5 Statistical Reasoning Questions
In Unit 5, there are three main topics (problem types): describing correlations, measuring correlations (with the r value) ...
Purdue Global University Unit 5 Statistical Reasoning Questions
In Unit 5, there are three main topics (problem types): describing correlations, measuring correlations (with the r value), and creating and evaluating scatterplots. You will be exposed to all three topics, and will have the opportunity to discuss and compare these topics with your fellow learners.Using any Excel dataset, choose two quantitative variables from the dataset. For example, you might choose “age” and “weight.” Next, do the following:What is the name of the dataset you have chosen? What are the names of the two quantitative variables you are investigating?Using Excel, calculate the relationship (correlation) between these two variables. Write down your calculated r value.Given the r value you calculated in number 1 above, explain what the r value tells you about the relationship between the two variables. For example, is the relationship positive or negative? Is the relationship strong, medium, or weak?Using the two variables you have chosen, create and attach to your post a scatterplot. Does the scatterplot have a linear appearance? What does the scatterplot tell you about the relationship between your two variables?Please create personalized and substantive responses to at least two other student main posts. In your response, include the following:Choose any two classmates and review their main posts.Find the dataset that each student used, and repeat their r value calculation in Excel for the variables they chose. Are they correct? Do you agree with how they described the relationship between the two variables? Explain in one paragraph and include both your results and the student results.For the same two classmates, also review their scatterplots. Discuss the “shape” of the scatterplot - is it mostly linear or is it all scattered around? Does the scatterplot seem to have a positive (upwards from left to right) or negative slope? Does their scatterplot visually match what the r value is suggesting? What can you say about the relationship based on the scatterplot?Discussion RequirementsReading and ResourcesRead the assigned chapters from the following textbooks:Bennett, J., Briggs, W.L. & Triola, M.F. (2013) Statistical Reasoning for Everyday Life (4th ed.). Upper Saddle, NJ: Pearson.Chapter 7: “Correlation and Causality”Reading the textbook and reviewing the textbook examples are excellent methods for starting each unit. Reading the textbook offers context and explanations for new concepts and methods. Completing the textbook examples on paper (and with Excel) is a great way to practice and learn the new methods and concepts introduced. Student feedback has suggested that reading the textbook and practicing the textbook examples has been particularly helpful if completed before the unit Seminar. Some students have reported that keeping a notebook handy, and recording new definitions or concepts encountered while reading is helpful, more organized, and stress reducing.This chapter includes a section that offers examples using technologies such as Excel. In addition, at the end of each chapter section, or at the end of the chapter, are review exercises that are very helpful for practicing and preparing.In this course, students may use Excel for any statistical calculations. Excel can be used to evaluate data in many ways. Excel can be used to calculate numerical measures, such as measures of center (such as mean and median) and measures of variation (variance, standard deviation, and range), as well as many other measures such as min, max, and correlation (r-value). Excel can also be used to create visualizations, such as histograms, bar graphs, pie graphs, scatterplots, and others. Excel may also be used to create linear regression equations. Because Excel is a very common tool, the Internet and YouTube both contain considerable support resources and tutorials. TEXTBOOKSBennett, J., Briggs, W., Triola, M. (2014) Statistical Reasoning: For Everyday Life.(4th ed)
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Grossmont Law Enforcement Officers Should Focus on Particular Social Groups Questions
This is the Example Prompt: The actual prompt is second.
Race vs Crime-Focus
We randomly selected 1,200 partic ...
Grossmont Law Enforcement Officers Should Focus on Particular Social Groups Questions
This is the Example Prompt: The actual prompt is second.
Race vs Crime-Focus
We randomly selected 1,200 participants form the 2020 Race IAT. Below is the two-way table for the sample with the row variable Race and the column variable Crime-Focus. Here is a description of each variable.
Race: 2020 Race- IAT participants' self-reported race.
Crime-Focus: 2020 Race-IAT participants' responses to the statement, "Law enforcement officers should pay particular attention to those social groups more heavily involved in crime, even if this means focusing on members of particular ethnic groups."
Race IAT: Race vs Crime-FocusStrongly DisagreeModerately DisagreeSlightly DisagreeNeutralSlightly
AgreeModerately
AgreeStrongly
AgreeRow TotalsAmer Indian/
Alaskan Native31101006East Asian1765130032South Asian1641120125Nat Hawaiian/
Pacific Islander30110005Black/
African Am511571101287White40018072831444121941Other/
Unknown2374445148Multiracial3463731256
EXAMPLE Neutrality about Crime-Focus
Are white IAT participants less likely than black participants to be neutral about law enforcement officers focusing their attention on particular social groups?
Work
In this question we are comparing the Black and White categories of the explanatory variable, Race. So we need to build two fractions with the totals for these explanatory variable categories in the denominators.
DenominatorsBlacksWhites?87?87?941?941The denominator is the total for Blacks.The denominator is the total for Whites.
Since we are asked about one category of the response variable, "neutral" we use the Neutral values in the numerators of the fractions.
NumeratorsBlacksWhites118711878394183941
The numerator, 11,
is "Neutral" given "Black."
The numerator, 83,
is "Neutral" given
"White."
Now we calculate and compare the percentages.
Blacks: 1187?0.126=12.6%1187?0.126=12.6%
Whites: 83941?0.088=8.8%83941?0.088=8.8%
The percentage of whites is less than the percentage of blacks. So, if the percent increase is 10% or greater, we will answer "yes" to the question, "Are white IAT participants less likely than black participants to be neutral about law enforcement officers focusing their attention on particular social groups?"
We need to calculate the percent increase from the smaller percentage to the larger percentage.
Step 1: Subtract the percentages (large - small).
12.6 - 8.8 = 3.8
Step 2: Divide the difference by the original amount and convert to a percent. Since we are calculating the percent increase from the smaller percentage to the larger percentage, the smaller percentage is the original amount.
3.88.8?0.432=43.2%3.88.8?0.432=43.2%
The percent increase is very large (more than 4 times greater than 10%), so we will answer "yes."
Answer
Approximately 8.8% of whites in the IAT sample are neutral about law enforcement officers focusing their attention on particular social groups. Whereas, approximately 12.6% of blacks are neutral. The percent increase from white participants in the IAT sample to black participants is 43.2%, so white IAT participants are less likely to be neutral than black participants.
EXAMPLE More likely to "strongly or moderately disagree"
Are black IAT participants more likely than East Asian participants to strongly or moderately disagree that law enforcement officers should focus their attention on particular social groups?
Work
In this question we are comparing the Black and East Asians categories of the explanatory variable, Race. So we need to build two fractions with the totals for these explanatory variable categories in the denominators.
DenominatorsBlacksEast Asians?87?87?32?32The denominator is the total for Blacks. The denominator is
the total for East
Asians.
We are asked about two categories of the response variable, "strongly disagree" or "moderately disagree." So, we need to add the values for each of these cells when conditioning on Black. And, we need to add the values for each of these cells when conditioning on East Asian.
NumeratorsBlacksEast Asians51+158751+158717+63217+632
The numerator,
51 + 15, is the sum of "Strongly Disagree" and "Moderately Disagree" given "Black."
The numerator,
17 + 6, is the sum
of "Strongly
Disagree" and
"Moderately
Disagree" given
"Black."
Now we calculate and compare the percentages.
Blacks: 51+1587=6687?0.759=75.9%51+1587=6687?0.759=75.9%
East Asians: 17+632=2332?0.719=71.9%17+632=2332?0.719=71.9%
The percentage of black IAt participants in the sample is greater than the percentage of East Asian participants. But, we do not yet know whether we can answer "yes" to the question, "Are black IAT participants more likely than East Asian participants to strongly or moderately disagree that law enforcement officers should focus their attention on particular social groups?"
We need to calculate the percent increase from the smaller percentage to the larger percentage.
Step 1: Subtract the percentages (large - small).
75.9 - 71.9 = 4
Step 2: Divide the difference by the original amount and convert to a percent. Since we are calculating the percent increase from the smaller percentage to the larger percentage, the smaller percentage is the original amount.
471.9?0.056=5.6%471.9?0.056=5.6%
The percent increase is too small! Since, 5.6% is less than 10%, we cannot answer "yes."
Answer
Approximately 75.9% of black IAT participants in the sample strongly or moderately disagree that law enforcement officers should focus their attention on particular social groups. Whereas, approximately 71.9% of East Asian participants strongly or moderately disagree. The percent increase from whites to East Asians is only 5.6%, so we cannot conclude that black IAT participants are more likely to strongly or moderately disagree. We randomly selected 1,200 participants form the 2020 Race IAT. Below is the two-way table for the sample with the row variable Race and the column variable Crime-Focus. Here is a description of each variable.
Actual Prompt:
We randomly selected 1,200 participants form the 2020 Race IAT. Below is the two-way table for the sample with the row variable Race and the column variable Crime-Focus. Here is a description of each variable.
Race: IAT participants' self-reported race.
Crime-Focus: IAT participants response to the statement, "Law enforcement officers should pay particular attention to those social groups more heavily involved in crime, even if this means focusing on members of particular ethnic groups."
Race IAT: Race vs Crime-Focus
Strongly DisagreeModerately DisagreeSlightly DisagreeNeutralSlightly
AgreeModerately
AgreeStrongly
AgreeRow TotalsAmer Indian/
Alaskan Native31101006East Asian1765130032South Asian1641120125Native Hawaiian/
Pacific Islander30110005Black/
African Am511571101287White40018072831444121941Other/
Unknown2374445148Multiracial3463731256
Respond to each of the following questions. Be sure to number your responses, show your work, and write your answers in context as we did on the previous page.
Are black IAT participants more likely than whites to strongly agree that law enforcement officers should focus their attention on particular social groups?
Are white IAT participants more likely than multiracial participants to moderately or strongly agree that law enforcement officers should focus their attention on particular social groups?
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