Data

tempmax=Flatten[Transpose[Import["m:\\modeling\\p2max.xlsx"]]];

time=N[Table[x/12,{x,1,39*12}]];

maxdata=Transpose[{time,tempmax}];

tempmin=Flatten[Transpose[Import["m:\\modeling\\p2min.xlsx"]]];

mindata=Transpose[{time,tempmin}];

Yearly Averages Max

Table[Total[Table[tempmax[[i]],{i,1+k,12+k}]],{k,0,38*12,12}]/12

moymax={33.1,32.7,33.1,33.3,33.5,32.9,33.7,33.4,33.1,33.3,34.4,33.5,33.2,33.7,33.2,33.2,33.2,33.6,33.9,34.0,33.6,34.4,33.9,33.9,34.5,34.2,34.1,33.9,34.1,34.5,34.0,33.9,34.2,34.5,34.8,33.9,33.8,34.3,34.7};

Total[moymax]

Total[Table[Total[Table[tempmax[[i]],{i,1+k,12+k}]],{k,0,38*12,12}]/12]

Yearly Averages Min

Table[Total[Table[tempmin[[i]],{i,1+k,12+k}]],{k,0,38*12,12}]/12

moymin={21.3,21.0,21.3,21.6,21.0,21.1,21.4,21.0,21.3,21.0,21.5,21.1,20.1,20.2,20.6,19.3,20.7,20.1,21.3,21.4,21.3,21.6,20.9,20.9,21.0,20.8,21.6,21.4,21.6,21.8,21.6,21.2,21.7,21.8,21.7,21.5,21.6,22.1,21.8};

Total[Table[Total[Table[tempmin[[i]],{i,1+k,12+k}]],{k,0,38*12,12}]/12]

Total[moymin]

Linear Model

Max Data

lm=LinearModelFit[maxdata,{x},x]
p1=ListPlot[maxdata];
p3=Plot[lm[x],{x,0,40},PlotStyle->Red];
Show[p1,p3]
lm["ParameterConfidenceIntervals"]
ListPlot[lm["FitResiduals"]]
lm["RSquared"]

Linear Model

Min Data

lmm=LinearModelFit[mindata,{x},x]
p2=ListPlot[mindata];
p4=Plot[lmm[x],{x,0,40},PlotStyle->Red];
Show[p2,p4]
lmm["ParameterConfidenceIntervals"]
ListPlot[lmm["FitResiduals"]]
lmm["RSquared"]

Time to kill the outlier

Ordering[tempmin,-1]

mindata=Delete[mindata,173];

Length[mindata]

lmm=LinearModelFit[mindata,{x},x]
p2=ListPlot[mindata];
p4=Plot[lmm[x],{x,0,40},PlotStyle->Red];
Show[p2,p4]
lmm["ParameterConfidenceIntervals"]
ListPlot[lmm["FitResiduals"]]
lmm["RSquared"]

Quadratic Model

Max Data

qm=LinearModelFit[maxdata,{x,(x^2)},x]
q1=Plot[qm[x],{x,0,40},PlotStyle->Red];
Show[p1,q1]
qm["ParameterConfidenceIntervals"]
ListPlot[qm["FitResiduals"]]
qm["RSquared"]

Min Data

qmm=LinearModelFit[mindata,{x,(x^2)},x]
q2=Plot[qmm[x],{x,0,40},PlotStyle->Red];
Show[p2,q2]
qmm["ParameterConfidenceIntervals"]
ListPlot[qmm["FitResiduals"]]
qmm["RSquared"]

Cubic Model

Min Data

cm=LinearModelFit[mindata,{x,(x^2),(x^3)},x]
c1=Plot[cm[x],{x,0,40},PlotStyle->Red];
Show[p2,c1]
cm["ParameterConfidenceIntervals"]
ListPlot[cm["FitResiduals"]]
cm["RSquared"]

Logistic Model

logm=LinearModelFit[mindata,{Log[x],x},x]
log1=Plot[logm[x],{x,0,40},PlotStyle->Red];
Show[p2,log1]
logm["ParameterConfidenceIntervals"]
ListPlot[logm["FitResiduals"]]
logm["RSquared"]

Cosine and Sine Model

Max Data

bbm=LinearModelFit[maxdata,{x,Cos[((2*π*x)/1)],Sin[((2*π*x)/1)]},x]
bb1=Plot[bbm[x],{x,0,40},PlotStyle->Red];
Show[p1,bb1]
bbm["ParameterConfidenceIntervals"]
ListPlot[bbm["FitResiduals"]]
bbm["RSquared"]

{a,b,c,d}=bbm["BestFitParameters"];
θ=ArcTan[(c/d)];
xp=((((π/2)-θ))/(2*π))
day=xp*365

Solve[D[bbm[x],x]==0,x]

Maximize[bbm[x],x]

perhaps check out the amplitude find the hottest day and coldest day of the year in kara

check on the outliers on the residuals and check whats going on

Min Data

ListLinePlot[Table[mindata[[i]],{i,1,40}]]

bm=LinearModelFit[mindata,{x,(x^2),Cos[((2*π*x)/1)],Sin[((2*π*x)/1)]},x]
b1=Plot[bm[x],{x,0,40},PlotStyle->Red];
Show[p2,b1]
bm["ParameterConfidenceIntervals"]
ListPlot[bm["FitResiduals"]]
bm["RSquared"]

be worrisome on the quadratic fit perhaps a model that levels off in the near future

The city of Kara has a busy market place, many luxury hotels, banks,
To the east are the Kabyés Mountains which will take you through the old colonial tracks to discover the local people and crafts ( potters and blacksmiths ).
In the West are Bassar and Bandjéli , famous for the work and extraction of iron.
In the South we will discover Bafilo and his traditional weavers 
Finally, the region is that of the reserves and natural parks that cover most of the territory: the Kéran National Park , the Aledjo reserve, the Oti reserve and finally the Sarakawa animal park .
The city of Kara has a busy market place, many luxury hotels, banks,