Using The Data Below, Put Your Data Into The Calculat 976400

Using The Data Below Put Your Data Into The Calculator Minutes In L1

Using the data below, put your data into the calculator (Minutes in L1 and Average in L2) then calculate the correlation coefficient (r ). What is r ? Round your answer to three decimal places. The math action research team at Sample school completed a fishbone diagram as part of the process to determine root cause of low math test scores. Teaching strategies is one of the possible causes. While investigating the various strategies; staff noticed that the test scores for one classroom were consistently higher that the others in the same grade. It was discovered that although all staff used the math games that accompany the curriculum; one teacher had scheduled regular use of the math games. Could this be the difference? Mrs. Alfred scheduled 40 minutes each week for her students to play math games. In addition, she has created a math game center. Students may attend this center each week for additional minutes of practice time. Mrs. Alfred agreed to record data over several units to help determine if there is a relationship between the regular use of math games and math test scores. She had the students keep track of the number of minutes they spent playing math games during the next unit. If the scatter plot shows a positive relationship – further investigation may be indicated. Student List Minutes of Math Games in 9 class days (80 class minutes) Average Test Grade % correct Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student Student

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Using The Data Below Put Your Data Into The Calculator Minutes In L1

In this analysis, we examine the relationship between the number of minutes students spent playing math games and their corresponding test scores. Mrs. Alfred's initiative to record the minutes spent in math games across several classrooms provides valuable data for a correlation analysis. The goal is to determine whether increased engagement with math games correlates positively with improved test scores, which could inform teaching strategies aimed at boosting student performance.

Data collection involved recording minutes of math game activity over nine class days for multiple students, alongside their average test scores expressed as percentages correct. The minutes recorded are within an 80-minute period, with some students engaging more frequently than others. This data is critical to understanding whether classroom activities have a measurable impact on academic achievement, particularly in math testing.

The core statistical method used here is calculating the Pearson correlation coefficient (r), which quantifies the strength and direction of a linear relationship between two variables: minutes spent in math games (independent variable) and test scores (dependent variable). An r value near +1 indicates a strong positive relationship, near -1 indicates a strong negative relationship, and near 0 suggests no relationship.

To compute r, first, the data is input into a calculator or statistical software, where the minutes are entered into dataset L1, and the test scores into dataset L2. The calculator then processes these to produce the correlation coefficient. This analysis helps to evaluate whether consistent use of math games could be a key factor in improving math scores.

Interpreting the scatter plot that accompanies this data can further guide conclusions. A positive trend would support the hypothesis that more frequent use of math games correlates with higher test scores, leading to considerations for more regular or additional use of math games in classroom instruction.

Ultimately, the findings from this data analysis should inform ongoing teaching strategies and resource allocation aimed at maximizing student learning outcomes in math. Further research and data collection can refine these insights, but initial correlation analysis provides a foundational understanding of potential impacts.

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