{"id":1177,"date":"2022-01-12T03:06:10","date_gmt":"2022-01-12T03:06:10","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/?post_type=chapter&p=1177"},"modified":"2022-02-07T23:22:55","modified_gmt":"2022-02-07T23:22:55","slug":"6a","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/chapter\/6a\/","title":{"raw":"6A","rendered":"6A"},"content":{"raw":"
\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Student First Name<\/strong><\/td>\r\nMidterm Score<\/strong>\r\n\r\n(out of 50 points)<\/strong><\/td>\r\nFinal Exam Score<\/strong>\r\n\r\n(out of 100 points)<\/strong><\/td>\r\n<\/tr>\r\n
Joe<\/td>\r\n42<\/td>\r\n64<\/td>\r\n<\/tr>\r\n
Barak<\/td>\r\n52<\/td>\r\n94<\/td>\r\n<\/tr>\r\n
Hillary<\/td>\r\n44<\/td>\r\n87<\/td>\r\n<\/tr>\r\n
Donald<\/td>\r\n25<\/td>\r\n46<\/td>\r\n<\/tr>\r\n
Cher<\/td>\r\n41<\/td>\r\n73<\/td>\r\n<\/tr>\r\n
Katy<\/td>\r\n39<\/td>\r\n73<\/td>\r\n<\/tr>\r\n
Taylor<\/td>\r\n33<\/td>\r\n53<\/td>\r\n<\/tr>\r\n
Miley<\/td>\r\n40<\/td>\r\n77<\/td>\r\n<\/tr>\r\n
Justin<\/td>\r\n35<\/td>\r\n60<\/td>\r\n<\/tr>\r\n
Snoop<\/td>\r\n31<\/td>\r\n62<\/td>\r\n<\/tr>\r\n
Bruno<\/td>\r\n37<\/td>\r\n71<\/td>\r\n<\/tr>\r\n
Kanye<\/td>\r\n49<\/td>\r\n95<\/td>\r\n<\/tr>\r\n
Leonardo<\/td>\r\n38<\/td>\r\n70<\/td>\r\n<\/tr>\r\n
Rosie<\/td>\r\n45<\/td>\r\n80<\/td>\r\n<\/tr>\r\n
Maya<\/td>\r\n49<\/td>\r\n80<\/td>\r\n<\/tr>\r\n
Tyra<\/td>\r\n48<\/td>\r\n82<\/td>\r\n<\/tr>\r\n
Selena<\/td>\r\n50<\/td>\r\n81<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Body mass (g)<\/strong><\/td>\r\nTOV <\/strong>\r\n\r\n(cm per second)<\/strong><\/td>\r\n<\/tr>\r\n
3640<\/td>\r\n334.5<\/td>\r\n<\/tr>\r\n
2670<\/td>\r\n387.3<\/td>\r\n<\/tr>\r\n
5600<\/td>\r\n410.8<\/td>\r\n<\/tr>\r\n
4130<\/td>\r\n318.6<\/td>\r\n<\/tr>\r\n
3020<\/td>\r\n368.7<\/td>\r\n<\/tr>\r\n
2660<\/td>\r\n358.8<\/td>\r\n<\/tr>\r\n
3240<\/td>\r\n344.6<\/td>\r\n<\/tr>\r\n
5140<\/td>\r\n324.6<\/td>\r\n<\/tr>\r\n
3690<\/td>\r\n301.4<\/td>\r\n<\/tr>\r\n
3620<\/td>\r\n331.8<\/td>\r\n<\/tr>\r\n
5310<\/td>\r\n312.6<\/td>\r\n<\/tr>\r\n
5560<\/td>\r\n316.8<\/td>\r\n<\/tr>\r\n
3970<\/td>\r\n375.6<\/td>\r\n<\/tr>\r\n
3770<\/td>\r\n372.4<\/td>\r\n<\/tr>\r\n
5100<\/td>\r\n314.3<\/td>\r\n<\/tr>\r\n
2950<\/td>\r\n367.5<\/td>\r\n<\/tr>\r\n
7930<\/td>\r\n286.3<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Week<\/strong><\/td>\r\nKai\u2019s weight<\/strong><\/td>\r\n<\/tr>\r\n
0<\/td>\r\n173<\/td>\r\n<\/tr>\r\n
1<\/td>\r\n171<\/td>\r\n<\/tr>\r\n
2<\/td>\r\n<\/td>\r\n<\/tr>\r\n
3<\/td>\r\n<\/td>\r\n<\/tr>\r\n
4<\/td>\r\n<\/td>\r\n<\/tr>\r\n
5<\/td>\r\n<\/td>\r\n<\/tr>\r\n
6<\/td>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Chirps per second<\/strong><\/td>\r\nTemperature in degrees Fahrenheit<\/strong><\/td>\r\n<\/tr>\r\n
20<\/td>\r\n88.6<\/td>\r\n<\/tr>\r\n
16<\/td>\r\n71.6<\/td>\r\n<\/tr>\r\n
19.8<\/td>\r\n93.3<\/td>\r\n<\/tr>\r\n
18.4<\/td>\r\n84.3<\/td>\r\n<\/tr>\r\n
17.1<\/td>\r\n80.6<\/td>\r\n<\/tr>\r\n
15.5<\/td>\r\n75.2<\/td>\r\n<\/tr>\r\n
14.7<\/td>\r\n69.7<\/td>\r\n<\/tr>\r\n
17.1<\/td>\r\n82<\/td>\r\n<\/tr>\r\n
15.4<\/td>\r\n69.4<\/td>\r\n<\/tr>\r\n
16.2<\/td>\r\n83.3<\/td>\r\n<\/tr>\r\n
15<\/td>\r\n79.6<\/td>\r\n<\/tr>\r\n
17.2<\/td>\r\n82.6<\/td>\r\n<\/tr>\r\n
16<\/td>\r\n80.6<\/td>\r\n<\/tr>\r\n
17<\/td>\r\n83.5<\/td>\r\n<\/tr>\r\n
14.4<\/td>\r\n76.3<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Skill or Concept: I can . . .<\/td>\r\nQuestions to check your understanding<\/td>\r\nRating\r\nfrom 1 to 5<\/td>\r\n<\/tr>\r\n
Identify when a linear regression analysis might be appropriate.<\/td>\r\n3, 4, 5 (Part A)<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Identify the explanatory and response variables in a given scenario.<\/td>\r\n1, 2, 5 (Parts B and C)<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Calculate the line of best fit and write it using proper notation.<\/td>\r\n5 (Parts D through F)<\/td>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n\"Two \"Several \"A \"A\r\n\r\nGlossary\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n
\r\n \t
Least Squares Regression (LSR) analysis<\/dt>\r\n \t
determining the equation of a line of best fit to make predictions based on an existing dataset, also be described as linear modeling.<\/dd>\r\n<\/dl>\r\n
\r\n \t
residual<\/dt>\r\n \t
a representation of how far off a prediction calculated from the line is compared to the actual, observed \ud835\udc66 value, illustrated by a vertical line; also called vertical error.<\/dd>\r\n<\/dl>","rendered":"
\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Student First Name<\/strong><\/td>\nMidterm Score<\/strong><\/p>\n

(out of 50 points)<\/strong><\/td>\n

Final Exam Score<\/strong><\/p>\n

(out of 100 points)<\/strong><\/td>\n<\/tr>\n

Joe<\/td>\n42<\/td>\n64<\/td>\n<\/tr>\n
Barak<\/td>\n52<\/td>\n94<\/td>\n<\/tr>\n
Hillary<\/td>\n44<\/td>\n87<\/td>\n<\/tr>\n
Donald<\/td>\n25<\/td>\n46<\/td>\n<\/tr>\n
Cher<\/td>\n41<\/td>\n73<\/td>\n<\/tr>\n
Katy<\/td>\n39<\/td>\n73<\/td>\n<\/tr>\n
Taylor<\/td>\n33<\/td>\n53<\/td>\n<\/tr>\n
Miley<\/td>\n40<\/td>\n77<\/td>\n<\/tr>\n
Justin<\/td>\n35<\/td>\n60<\/td>\n<\/tr>\n
Snoop<\/td>\n31<\/td>\n62<\/td>\n<\/tr>\n
Bruno<\/td>\n37<\/td>\n71<\/td>\n<\/tr>\n
Kanye<\/td>\n49<\/td>\n95<\/td>\n<\/tr>\n
Leonardo<\/td>\n38<\/td>\n70<\/td>\n<\/tr>\n
Rosie<\/td>\n45<\/td>\n80<\/td>\n<\/tr>\n
Maya<\/td>\n49<\/td>\n80<\/td>\n<\/tr>\n
Tyra<\/td>\n48<\/td>\n82<\/td>\n<\/tr>\n
Selena<\/td>\n50<\/td>\n81<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Body mass (g)<\/strong><\/td>\nTOV <\/strong><\/p>\n

(cm per second)<\/strong><\/td>\n<\/tr>\n

3640<\/td>\n334.5<\/td>\n<\/tr>\n
2670<\/td>\n387.3<\/td>\n<\/tr>\n
5600<\/td>\n410.8<\/td>\n<\/tr>\n
4130<\/td>\n318.6<\/td>\n<\/tr>\n
3020<\/td>\n368.7<\/td>\n<\/tr>\n
2660<\/td>\n358.8<\/td>\n<\/tr>\n
3240<\/td>\n344.6<\/td>\n<\/tr>\n
5140<\/td>\n324.6<\/td>\n<\/tr>\n
3690<\/td>\n301.4<\/td>\n<\/tr>\n
3620<\/td>\n331.8<\/td>\n<\/tr>\n
5310<\/td>\n312.6<\/td>\n<\/tr>\n
5560<\/td>\n316.8<\/td>\n<\/tr>\n
3970<\/td>\n375.6<\/td>\n<\/tr>\n
3770<\/td>\n372.4<\/td>\n<\/tr>\n
5100<\/td>\n314.3<\/td>\n<\/tr>\n
2950<\/td>\n367.5<\/td>\n<\/tr>\n
7930<\/td>\n286.3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n\n\n\n\n\n\n\n\n\n\n
Week<\/strong><\/td>\nKai\u2019s weight<\/strong><\/td>\n<\/tr>\n
0<\/td>\n173<\/td>\n<\/tr>\n
1<\/td>\n171<\/td>\n<\/tr>\n
2<\/td>\n<\/td>\n<\/tr>\n
3<\/td>\n<\/td>\n<\/tr>\n
4<\/td>\n<\/td>\n<\/tr>\n
5<\/td>\n<\/td>\n<\/tr>\n
6<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Chirps per second<\/strong><\/td>\nTemperature in degrees Fahrenheit<\/strong><\/td>\n<\/tr>\n
20<\/td>\n88.6<\/td>\n<\/tr>\n
16<\/td>\n71.6<\/td>\n<\/tr>\n
19.8<\/td>\n93.3<\/td>\n<\/tr>\n
18.4<\/td>\n84.3<\/td>\n<\/tr>\n
17.1<\/td>\n80.6<\/td>\n<\/tr>\n
15.5<\/td>\n75.2<\/td>\n<\/tr>\n
14.7<\/td>\n69.7<\/td>\n<\/tr>\n
17.1<\/td>\n82<\/td>\n<\/tr>\n
15.4<\/td>\n69.4<\/td>\n<\/tr>\n
16.2<\/td>\n83.3<\/td>\n<\/tr>\n
15<\/td>\n79.6<\/td>\n<\/tr>\n
17.2<\/td>\n82.6<\/td>\n<\/tr>\n
16<\/td>\n80.6<\/td>\n<\/tr>\n
17<\/td>\n83.5<\/td>\n<\/tr>\n
14.4<\/td>\n76.3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n\n\n\n\n\n\n
Skill or Concept: I can . . .<\/td>\nQuestions to check your understanding<\/td>\nRating
\nfrom 1 to 5<\/td>\n<\/tr>\n
Identify when a linear regression analysis might be appropriate.<\/td>\n3, 4, 5 (Part A)<\/td>\n<\/td>\n<\/tr>\n
Identify the explanatory and response variables in a given scenario.<\/td>\n1, 2, 5 (Parts B and C)<\/td>\n<\/td>\n<\/tr>\n
Calculate the line of best fit and write it using proper notation.<\/td>\n5 (Parts D through F)<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\"Two \"Several \"A \"A<\/p>\n

Glossary<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

\n
Least Squares Regression (LSR) analysis<\/dt>\n
determining the equation of a line of best fit to make predictions based on an existing dataset, also be described as linear modeling.<\/dd>\n<\/dl>\n
\n
residual<\/dt>\n
a representation of how far off a prediction calculated from the line is compared to the actual, observed \ud835\udc66 value, illustrated by a vertical line; also called vertical error.<\/dd>\n<\/dl>\n","protected":false},"author":23592,"menu_order":20,"template":"","meta":{"_candela_citation":"[]","CANDELA_OUTCOMES_GUID":"","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-1177","chapter","type-chapter","status-publish","hentry"],"part":704,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/1177","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/users\/23592"}],"version-history":[{"count":4,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/1177\/revisions"}],"predecessor-version":[{"id":2902,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/1177\/revisions\/2902"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/parts\/704"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/1177\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/media?parent=1177"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapter-type?post=1177"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/contributor?post=1177"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/license?post=1177"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}