#intervalsprints

[revolttoevolve]

Tempo tempo tempo my nemesis. It hurts, it’s tough going but damn it, it’s beneficial. My issue is maintaining the pace because I always end up running too fast. Eventually I will get there but it’s not a friendly journey. #tempo #tempohell #intervals #sprint #intervalsprints #speed #speedwork #ultratraining #ultrarunning https://www.instagram.com/p/CiNs-qIo81x/?igshid=NGJjMDIxMWI=

[extremerunning]

Beginning exercise has been known for some time to help those with depression in reducing anxiety and improving mood. This is the first study showing the effect of stopping exercise. . . . #hiitworkout #personaltrainer #personaltraining #liverpool #merseyside #intervalsprints #prescot #huyton #pt #onetoonesessions #grouptraining #weightloss #weights #liftheavy #bodyfatreduction #fatburners #dm #tbt #crossfitathlete #crossfitosasco #crossfitfriends #crossfitforlife #crossfitwod #crossfitmen #messageforinfo #crossfitlife #crossfitbox #dietplans #crossfitlifestyle #crossfitcommunity https://www.instagram.com/p/BnlGmDen54T/?utm_source=ig_tumblr_share&igshid=my8jfu4qatgc

[amycainefitness]

Making progress is about stepping up and challenging yourself. Decided today was a good day to bump up my sprints -> because if it doesn’t challenge you, it doesn’t change you, right?? #HIIT #sprints #intervals #intervalsprints #elliptical #ellipticalworkout #carbcycling #lowcarbday (at Club Siena DB)

[spartan682]

Free rorschach test with every workout. #intervalsprints #getafterit #strongereveryday

[ptcentre]

Today's Bootcamp hill run and interval sprint session ended with a cool down in the pool (30degrees). 💦💧 . . #summerbody #summer #summertimeshine #fitness #fitfam #outdoorexercise #running #hillrunning #intervalsprints #oxshott #ladies #bootcamp #surreybootcamp #thepersonaltrainingcentre #personaltrainingstudio

[wldata]

Assignment 4

#First, change the dependent variable, price, to binary:

kc['price_r'] = kc['price'] for i in range(0,len(kc['price'])):       if kc['price'][i] > median:              kc['price_r'][i]=1       else:    kc['price_r'][i]=0

#Then, performa logistic regression with two variables: living room area and the parking lot area:

lreg2=smf.logit(formula = 'price_r ~ sqft_living_c + sqft_lot_c',data=kc).fit()print(lreg2.summary())

The result shows the parking lot p value is high, indicating the confidence is low for this variable.

#odd ratios with 95% confidence 

intervalsprint("odds ratios") params = lreg2.params conf = lreg2.conf_int() conf['OR']=params conf.columns = ['lower CI','upper CI','OR'] print(np.exp(conf))

From the odd ratios, it can’t tell which variables have a better correlation with the binary price variable. 

[saltysockmonkey]

C3W4 Logistic Regression

My hypothesis is to find out if there is a relationship between life expectancy and urban rate. Life expectancy is the response variable, while urban rate is the explanatory variable. Since the gapminder data set variables are all quantitative, I needed to create a new data frame for the variables and bin each into 2 categories. The 2 categories are based on each variable's mean value. For each variable, 1 is >= the variable;s mean while all else is 0.

The initial regression model shows there is a statistical relationship between life expectancy and urban rate (p=1.632e-11, OR=12.10, 95% CI=26.98). Potential confounding factors include HIV Rate, alcohol consumption, and income per person. 

It was found that incomerate has no statistical relationship in this model based on the p-value being 0.998. HIV Rate has a significant association with life expectancy (p=1.563e-10), but HIV has a lower OR (0.03). So HIV does have a significant relationship with life expectancy, but in an urban area the odds are low it will affect life expectancy. Alcohol consumption has a 3 times higher affect on life expectancy (OR=3.18, p=0.021, 95% CI=8.52) in urban areas even though it is less statistically significant than HIV Rate.

So the model output shows that life expectancy has significant statistical associations with urban rate and the confounding variables of HIV rate and alcohol consumption.

Complete OUTPUT

Life Expectancy Categories0    621    82Name: LIFE1, dtype: int64Urban Rate Categories0    711    73Name: URB1, dtype: int64HIV Rate Categories0    1151     29Name: HIV1, dtype: int64Alcohol Consumption Rate Categories0.00    761.00    68Name: ALCO1, dtype: int64Income Per Person Rate Categories0    1091     35Name: INC1, dtype: int64Optimization terminated successfully.         Current function value: 0.525941         Iterations 6                           Logit Regression Results                           ==============================================================================Dep. Variable:                  LIFE1   No. Observations:                  144Model:                          Logit   Df Residuals:                      142Method:                           MLE   Df Model:                            1Date:                Sat, 10 Oct 2020   Pseudo R-squ.:                  0.2305Time:                        09:21:51   Log-Likelihood:                -75.736converged:                       True   LL-Null:                       -98.420Covariance Type:            nonrobust   LLR p-value:                 1.632e-11==============================================================================                 coef    std err          z      P>|z|      [0.025      0.975]------------------------------------------------------------------------------Intercept     -0.8675      0.260     -3.336      0.001      -1.377      -0.358URB1           2.4935      0.409      6.095      0.000       1.692       3.295==============================================================================Odds Ratios for LIFE1 to URB1Intercept    0.42URB1        12.10dtype: float64COnfiedence Intervals for LIFE1 to URB1           Lower CI  Upper CI    ORIntercept      0.25      0.70  0.42URB1           5.43     26.98 12.10Optimization terminated successfully.         Current function value: 0.541289         Iterations 7                           Logit Regression Results                           ==============================================================================Dep. Variable:                  LIFE1   No. Observations:                  144Model:                          Logit   Df Residuals:                      142Method:                           MLE   Df Model:                            1Date:                Sat, 10 Oct 2020   Pseudo R-squ.:                  0.2080Time:                        09:21:51   Log-Likelihood:                -77.946converged:                       True   LL-Null:                       -98.420Covariance Type:            nonrobust   LLR p-value:                 1.563e-10==============================================================================                 coef    std err          z      P>|z|      [0.025      0.975]------------------------------------------------------------------------------Intercept      0.8267      0.203      4.079      0.000       0.429       1.224HIV1          -3.4294      0.760     -4.510      0.000      -4.920      -1.939==============================================================================Odds Ratios for LIFE1 to HIV1Intercept   2.29HIV1        0.03dtype: float64COnfiedence Intervals for LIFE1 to HIV1           Lower CI  Upper CI   ORIntercept      1.54      3.40 2.29HIV1           0.01      0.14 0.03Optimization terminated successfully.         Current function value: 0.622369         Iterations 5                           Logit Regression Results                           ==============================================================================Dep. Variable:                  LIFE1   No. Observations:                  144Model:                          Logit   Df Residuals:                      142Method:                           MLE   Df Model:                            1Date:                Sat, 10 Oct 2020   Pseudo R-squ.:                 0.08940Time:                        09:21:51   Log-Likelihood:                -89.621converged:                       True   LL-Null:                       -98.420Covariance Type:            nonrobust   LLR p-value:                 2.730e-05==============================================================================                 coef    std err          z      P>|z|      [0.025      0.975]------------------------------------------------------------------------------Intercept     -0.3727      0.233     -1.597      0.110      -0.830       0.085ALCO1          1.4713      0.365      4.036      0.000       0.757       2.186==============================================================================Odds Ratios for LIFE1 to ALCO1Intercept   0.69ALCO1       4.35dtype: float64COnfiedence Intervals for LIFE1 to ALCO1           Lower CI  Upper CI   ORIntercept      0.44      1.09 0.69ALCO1          2.13      8.90 4.35Warning: Maximum number of iterations has been exceeded.         Current function value: 0.517484         Iterations: 35                           Logit Regression Results                           ==============================================================================Dep. Variable:                  LIFE1   No. Observations:                  144Model:                          Logit   Df Residuals:                      142Method:                           MLE   Df Model:                            1Date:                Sat, 10 Oct 2020   Pseudo R-squ.:                  0.2429Time:                        09:21:51   Log-Likelihood:                -74.518converged:                      False   LL-Null:                       -98.420Covariance Type:            nonrobust   LLR p-value:                 4.710e-12==============================================================================                 coef    std err          z      P>|z|      [0.025      0.975]------------------------------------------------------------------------------Intercept     -0.2770      0.193     -1.432      0.152      -0.656       0.102INC1          22.0742   9144.697      0.002      0.998   -1.79e+04    1.79e+04==============================================================================

Possibly complete quasi-separation: A fraction 0.24 of observations can beperfectly predicted. This might indicate that there is completequasi-separation. In this case some parameters will not be identified.Odds Ratios for LIFE1 to INC1Intercept            0.76INC1        3861007201.13dtype: float64COnfiedence Intervals for LIFE1 to INC1           Lower CI  Upper CI            ORIntercept      0.52      1.11          0.76INC1           0.00       inf 3861007201.13Optimization terminated successfully.         Current function value: 0.411816         Iterations 7                           Logit Regression Results                           ==============================================================================Dep. Variable:                  LIFE1   No. Observations:                  144Model:                          Logit   Df Residuals:                      140Method:                           MLE   Df Model:                            3Date:                Sat, 10 Oct 2020   Pseudo R-squ.:                  0.3975Time:                        09:21:51   Log-Likelihood:                -59.301converged:                       True   LL-Null:                       -98.420Covariance Type:            nonrobust   LLR p-value:                 7.332e-17==============================================================================                 coef    std err          z      P>|z|      [0.025      0.975]------------------------------------------------------------------------------Intercept     -0.6005      0.322     -1.863      0.062      -1.232       0.031URB1           2.0323      0.488      4.163      0.000       1.075       2.989ALCO1          1.1580      0.502      2.305      0.021       0.173       2.143HIV1          -3.4899      0.848     -4.114      0.000      -5.152      -1.827==============================================================================           Lower CI  Upper CI   ORIntercept      0.29      1.03 0.55URB1           2.93     19.87 7.63ALCO1          1.19      8.52 3.18HIV1           0.01      0.16 0.03Code -------------------------------------------------------------------------

CODE import pandas as pdimport numpy as npimport seaborn as sbimport statsmodels.formula.api as smfimport statsmodels.stats.multicomp as multiimport scipy.stats as statsimport matplotlib.pyplot as plt # bug fix for display formats to avoid run time errorspd.set_option('display.float_format', lambda x:'%.2f'%x) gmdata = pd.read_csv('gapminder.csv', low_memory=False) ### Data Management ### # convert to numericgmdata.lifeexpectancy = gmdata.lifeexpectancy.replace(" " ,np.nan)gmdata.lifeexpectancy = pd.to_numeric(gmdata.lifeexpectancy)gmdata.urbanrate = gmdata.urbanrate.replace(" " ,np.nan)gmdata.urbanrate = pd.to_numeric(gmdata.urbanrate)gmdata.incomeperperson = gmdata.incomeperperson.replace(" " ,np.nan)gmdata.incomeperperson = pd.to_numeric(gmdata.incomeperperson)gmdata.alcconsumption = gmdata.alcconsumption.replace(" " ,np.nan)gmdata.alcconsumption = pd.to_numeric(gmdata.alcconsumption)gmdata.hivrate = gmdata.hivrate.replace(" " ,np.nan)gmdata.hivrate = pd.to_numeric(gmdata.hivrate) sub1 = gmdata[['urbanrate', 'lifeexpectancy', 'alcconsumption', 'incomeperperson', 'hivrate']].dropna() ## Drop all rows with NANsub1.lifeexpectancy.dropna()sub1.urbanrate.dropna()sub1.hivrate.dropna()sub1.incomeperperson.dropna()sub1.alcconsumption.dropna() #data check#a=sub1#print(a) #print("Life Expectancy Deviation")#desc1=gmdata.lifeexpectancy.describe()#print(desc1) #print("Urban Rate Deviation")#desc2=gmdata.urbanrate.describe()#print(desc2) #print("HIV Rate Deviation")#desc3=gmdata.hivrate.describe()#print(desc3) #print("Alchohol COnsumption Deviation")#desc4=gmdata.alcconsumption.describe()#print(desc4) #print("Income Rate Deviation")#desc5=gmdata.incomeperperson.describe()#print(desc5) # build bin for response categoriesdef LIFE1(row):    if row['lifeexpectancy'] >= 69.75:        return 1    else:        return 0  print ("Life Expectancy Categories")sub1['LIFE1'] = gmdata.apply (lambda row: LIFE1 (row),axis=1)chk1 = sub1['LIFE1'].value_counts(sort=False, dropna=False)print(chk1)#def URB1(row):    if row['urbanrate'] >= 56.77:        return 1    else:        return 0print ("Urban Rate Categories")sub1['URB1'] = gmdata.apply (lambda row: URB1 (row),axis=1)chk2 = sub1['URB1'].value_counts(sort=False, dropna=False)print(chk2)#def HIV1(row):    if row['hivrate'] >= 1.94:        return 1    else:        return 0print ("HIV Rate Categories")sub1['HIV1'] = gmdata.apply (lambda row: HIV1 (row),axis=1)chk3 = sub1['HIV1'].value_counts(sort=False, dropna=False)print(chk3)#def ALCO1(row):    if row['alcconsumption'] > 6.69:        return 1    if row['alcconsumption'] <6.70:        return 0     print ("Alcohol Consumption Rate Categories")sub1['ALCO1'] = gmdata.apply (lambda row: ALCO1 (row),axis=1)chk4 = sub1['ALCO1'].value_counts(sort=False, dropna=False)print(chk4)#def INC1(row):    if row['incomeperperson'] >= 8740.97:        return 1    else:        return 0print ("Income Per Person Rate Categories")sub1['INC1'] = gmdata.apply (lambda row: INC1 (row),axis=1)chk5 = sub1['INC1'].value_counts(sort=False, dropna=False)print(chk5) #Check Bins#print(sub1) ###End Data Managament## ## Logistic Regression for individual variables against Life Expectancy### logistic regression with URB1 ratelreg1 = smf.logit(formula = 'LIFE1 ~ URB1', data = sub1).fit()print (lreg1.summary()) # odds ratiosprint ("Odds Ratios for LIFE1 to URB1")print (np.exp(lreg1.params)) # odd ratios with 95% confidence intervalsprint("COnfiedence Intervals for LIFE1 to URB1")params = lreg1.paramsconf = lreg1.conf_int()conf['OR'] = paramsconf.columns = ['Lower CI', 'Upper CI', 'OR']print (np.exp(conf))###LREG2lreg2 = smf.logit(formula = 'LIFE1 ~ HIV1', data = sub1).fit()print (lreg2.summary()) # odds ratiosprint ("Odds Ratios for LIFE1 to HIV1")print (np.exp(lreg2.params)) # odd ratios with 95% confidence intervalsprint("COnfiedence Intervals for LIFE1 to HIV1")params2 = lreg2.paramsconf2 = lreg2.conf_int()conf2['OR'] = params2conf2.columns = ['Lower CI', 'Upper CI', 'OR']print (np.exp(conf2)) #LREG3lreg3 = smf.logit(formula = 'LIFE1 ~ ALCO1', data = sub1).fit()print (lreg3.summary()) # odds ratiosprint ("Odds Ratios for LIFE1 to ALCO1")print (np.exp(lreg3.params)) # odd ratios with 95% confidence intervalsprint("COnfiedence Intervals for LIFE1 to ALCO1")params3 = lreg3.paramsconf3 = lreg3.conf_int()conf3['OR'] = params3conf3.columns = ['Lower CI', 'Upper CI', 'OR']print (np.exp(conf3)) #LREG4lreg4 = smf.logit(formula = 'LIFE1 ~ INC1', data = sub1).fit()print (lreg4.summary()) # odds ratiosprint ("Odds Ratios for LIFE1 to INC1")print (np.exp(lreg4.params)) # odd ratios with 95% confidence intervalsprint("COnfiedence Intervals for LIFE1 to INC1")params4 = lreg4.paramsconf4 = lreg4.conf_int()conf4 ['OR'] = params4conf4.columns = ['Lower CI', 'Upper CI', 'OR']print (np.exp(conf4))####### Logistic Regression for multiple variables against Life Expectancy##lreg5 = smf.logit(formula = 'LIFE1 ~ URB1 + ALCO1 + HIV1', data = sub1).fit()print (lreg5.summary()) # odd ratios with 95% confidence intervalsparams5 = lreg5.paramsconf5 = lreg5.conf_int()conf5 ['OR'] = params5conf5.columns = ['Lower CI', 'Upper CI', 'OR']print (np.exp(conf5))

[ldziewiecki]

Wiem, że w kółko to samo ale zamiast kilometrów na bieżni wystarczy porządny HIT. Ja akurat preferuje 1 min full speed 2 min na spokojnie. To tak na początek. . . #intervaller #intervalrun #intervalinho #treinointervalado #intervaltraining #intervallfasten #intervalos #intervalado #intervalrunning #interval #intervalworkout #intervales #highintensityintervaltraining #intervalcardio #intervalle #intervall #intervaltræning #intervallfasten168 #intervalltrening #intervalli #intervallträning #surfaceinterval #intervallo #ultraintervalchallenge #prilaga #intervalo #intervals #intervalltraining #intervalsprints #intervalls (w: Poland) https://www.instagram.com/p/B42qNeMJiwh/?igshid=3vifbu81rkl7

[edgeperformanceclt]

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Ropes and rowers! #morningworkoutsarethebest #morningworkout #battlingropes #concept2 #intervalsprints #stayinshape (at EDGE Performance Training, LLC)

[scottmcmartin]

638 calories tonight in an hour at gym. 4 mins of incline walking and 6 min sprint intervals (much faster than my wife thought!). Then into 45 min hiit class. #burst #fitbit #chargehr @xercise4less @fitbit #hiit #intervalsprints #weightlossjourney (at Xercise4less Falkirk)

[amyjayne]

Hill sprints. Don't let that corner and mild gradient fool you! #misleading #hillsprints #intervalsprints #intervaltraining #fitness #fitspo #fitstagram #driveway #homefitness

[amycainefitness]

My sprints kicked my butt so good that apparently I couldn’t even hold my eyelids open 😆 Seriously, elliptical sprints only look easy! #sprints #elliptical #sprintworkout #intervals #intervalsprints #intervalworkout #HIIT #ellipticalworkout #lowcarbday #carbcycling #speedbursts

[kevinlingle]

And to think I was finally done with long sleeves and leggings for the summer. #springinpa #runhappy #run #intervalsprints

[mikecaulo]

#HighAltitude #IntervalSprints #lifeofafighter #hwpo ... Hope my opponent is training for this one caz I'm bringing the heat bell to bell (Taken with Instagram)

[edgeperformanceclt]

Ropin it up at EDGE! #battlingropes #morningworkout #morningworkoutsarethebest #intervalsprints

[amycainefitness]

Kicking off a new week with another round of 10 interval swim sprints. I like interval workouts because they’re tough but doable AND they crank up my fat burner for the rest of the day! �� I also love that there are so many ways to do interval workouts so you can make the workouts your own. #noexcuses #intervalsprints #swimming #swimsprints #fastedcardio #lowcarbworkout #carbcycling #hongkong #discoverybay #clubsiena