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Stats141XP Assessment of Statistical Literacy

创建日期：2024-04-27 18:02:00

所属分类：统计学statistics

标签： Stats141XP

Assessment of Statistical Literacy

Stats141XP
Assessment of Statistical Literacy

I give my word of honor to complete this exam on my own and not contact any of my classmates
or anybody else for help.
Signature
1
1 Question 1
See article “Lonliness, social isolation, and pain following the COVID-19 outbreak” and answer the
following prompts. Figures 3 and 4 from the article are presented below.
you need to explain the application of multinomial
regression. Using figures 3 and 4, answer the following questions.
1. What is the outcome?
2. What are the predictors?
3. Interpret the intervals for headache within context.
4. Can you explain how many logistic models results from figure four? Explain what they are?
5. State the research question underlying figures three and four within context.
6. Comparing figures three and four, interpret the confidence intervals of always having headache
prior to and during covid-19 pandemic within context.
7. If you wanted to summarize the association between social isolation, and pain following the
COVID-19 outbreak in Japan, in five lines, what would you say.
ͷͶ
Vol:.(1234567890)
Ƥ | (2021) 11:18643 | ǣȀȀǤȀͷͶǤͷͶ͹;ȀͺͷͻͿ;ǦͶ͸ͷǦͿͽͷ͹ͼǦ͹
www.nature.com/scientificreports/
ȀǤ Table 2, Figs. 1, 2, 3, and 4 indicate that both loneliness and the perception
of increased social isolation during the pandemic were positively associated with the prevalence and incidence of
all types of pain (i.e., headache, neck or shoulder pain, upper limb pain, low back pain, and leg pain).
Ǥ Table 3 and Fig. 5 indicate the di"erences in reported pain intensity according to the
UCLA-LS3-SF3 scoring groups and the frequency with which participants reported an increase in feelings of
social isolation. Compared to participants who did not experience loneliness or increased social isolation, those
Figure 3. Frequency of feelings of social isolation and pain symptoms since before the COVID-19 outbreak.
Odds ratios of each pain symptom in model 2 according to the frequency of feelings of social isolation
compared to never felt feelings of social isolation were indicated. X-axis indicated odds ratio. Bars indicated 95%
con#dence intervals. Abbreviation: OR odds ratio.
Figure 4. Frequency of feelings of social isolation and pain symptoms developed during the COVID-19
pandemic. Odds ratios of each pain symptom in model 2 according to the frequency of feelings of social
isolation compared to never felt feelings of social isolation were indicated. X-axis indicated odds ratio. Bars
indicated 95% con#dence intervals. Abbreviation: OR odds ratio.
2
ͷͶ
Vol:.(1234567890)
Ƥ | (2021) 11:18643 | ǣȀȀǤȀͷͶǤͷͶ͹;ȀͺͷͻͿ;ǦͶ͸ͷǦͿͽͷ͹ͼǦ͹
www.nature.com/scientificreports/
ȀǤ Table 2, Figs. 1, 2, 3, and 4 indicate that both loneliness and the perception
of increased social isolation during the pandemic were positively associated with the prevalence and incidence of
all types of pain (i.e., headache, neck or shoulder pain, upper limb pain, low back pain, and leg pain).
Ǥ Table 3 and Fig. 5 indicate the di"erences in reported pain intensity according to the
UCLA-LS3-SF3 scoring groups and the frequency with which participants reported an increase in feelings of
social isolation. Compared to participants who did not experience loneliness or increased social isolation, those
Figure 3. Frequency of feelings of social isolation and pain symptoms since before the COVID-19 outbreak.
Odds ratios of each pain symptom in model 2 according to the frequency of feelings of social isolation
compared to never felt feelings of social isolation were indicated. X-axis indicated odds ratio. Bars indicated 95%
con#dence intervals. Abbreviation: OR odds ratio.
Figure 4. Frequency of feelings of social isolation and pain symptoms developed during the COVID-19
pandemic. Odds ratios of each pain symptom in model 2 according to the frequency of feelings of social
isolation compared to never felt feelings of social isolation were indicated. X-axis indicated odds ratio. Bars
indicated 95% con#dence intervals. Abbreviation: OR odds ratio.
2 Question 2
The objective of a study is to examine the effect of three diet programs on men and women while
controlling for the pre-weight. Given the data set called diet.csv, answer the following questions:
1. Conduct the appropriate analysis.
2. Check the relevant assumptions.
3. If need be conduct and interpret the relevant post-hocs.
4. Write the linear model for the study.
5. Interpret the results within context.
6. Does the study have practical significance. Back up your conclusion by doing the relevant
calculations and interpretations.
7. Suppose that in addition to controlling for pre-weight, you also control for gender, age, and
height. Would the practical significance of the diet program improve? Yes or no and explain
why.
3
3 Question 3
Suppose that you were the TA for this class, how would you explain/summarize the following ROC
curve and table for your students? Elaborate what AUC, accuracy MSE, RMSE, MAE indicate.

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关键词一关键词二关键词三关键词四关键词五和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 MGMTMFE 405 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COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 CIVL2010 ECMT2150 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